[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Survey on extreme learning machines for outlier detection

  • Published:
Machine Learning Aims and scope Submit manuscript

Abstract

In a two-class classification task, if the number of examples of one class (majority) is much greater than that of another class (minority), then the classification is said to be class imbalanced. It can occur among many real-world applications, such as intrusion detection, medical diagnosis, etc. The class imbalance issue can make learning difficult since learning opts to bias towards the majority class. Outliers are cases with anomalous behaviors and are extreme cases of class imbalance. Despite late advances in extreme learning machines (ELMs), there are not many experimental investigations in the field of ELM with outlier detection. In this survey, we provide a comprehensive overview of existing ELMs to address the problem of outlier detection under a unified perspective. Firstly, we describe the background of our work, which includes a brief overview of previous surveys and a detailed description of the enhanced ELMs. Next, available studies regarding why ELMs are used to tackle the class imbalance problem are reviewed. Furthermore, cutting-edge algorithms are surveyed for improved ELMs to detect outliers. We classify these methods under three different machine learning perspectives (i.e., supervised, unsupervised, and semi-supervised approaches). In addition, we explore the developments of existing solutions based on three standardized quality metrics (i.e., accuracy, robustness, and speed) and other performance metrics (e.g., mean absolute percentage error and mean absolute error). After that, related datasets are detailed to facilitate future studies in this field. Last but the most important, this study concludes with discussions, challenges, and suggestions to guide future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

Not applicable.

Code Availability

Not applicable.

References

  • Abdelghafar, S., Darwish, A., Hassanien, A. E., Yahia, M., & Zaghrout, A. (2019). Anomaly detection of satellite telemetry based on optimized extreme learning machine. Journal of Space Safety Engineering, 6(4), 291–298.

    Google Scholar 

  • Adeli, E., Thung, K. H., An, L., Wu, G., Shi, F., Wang, T., & Shen, D. (2018). Semi-supervised discriminative classification robust to sample-outliers and feature-noises. IEEE transactions on pattern analysis and machine intelligence, 41(2), 515–522.

    Google Scholar 

  • Aggarwal, C. C. (2017). An introduction to outlier analysis. Outlier analysis (pp. 1–34). Springer.

  • Aggarwal, C. C., & Yu, P. S. (2001, May). Outlier detection for high dimensional data. In Proceedings of the 2001 ACM SIGMOD international conference on Management of data (pp. 37–46).

  • Aggarwal, C. C., & Yu, P. S. (2008, April). Outlier detection with uncertain data. In Proceedings of the 2008 SIAM International Conference on Data Mining (pp. 483–493). Society for Industrial and Applied Mathematics.

  • Aggarwal, C. C., Zhao, Y., & Philip, S. Y. (2011, April). Outlier detection in graph streams. In 2011 IEEE 27th international conference on data engineering (pp. 399–409). IEEE.

  • Albuquerque Filho, J. E., Brandão, L. C., Fernandes, B. J., & Maciel, A. M. (2022). A review of neural networks for Anomaly Detection. IEEE Access: Practical Innovations, Open Solutions, 10, 112342–112367.

    Google Scholar 

  • Alcin, O. F., Ucar, F., & Korkmaz, D. (2016, August). Extreme learning machine based robotic arm modeling. In 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR) (pp. 1160–1163). IEEE.

  • Altunay, H. C., Albayrak, Z., Özalp, A. N., & Çakmak, M. (2021, June). Analysis of anomaly detection approaches performed through deep learning methods in SCADA systems. In 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1–6). IEEE.

  • Anjaneyulu, M., & Kishore, A. P. A. U. (2019). Financial Fraud detection with Anomaly Feature detection on credit card. International Journal of Scientific Research & Engineering, 5(3), 835–837.

    Google Scholar 

  • Ariafar, E., & Kiani, R. (2017, December). Intrusion detection system using an optimized framework based on data mining techniques. In 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI) (pp. 0785–0791). IEEE.

  • Balasundaram, S. (2013). On extreme learning machine for ε-insensitive regression in the primal by Newton method. Neural Computing and Applications, 22(3), 559–567.

    Google Scholar 

  • Barreto, G. A., & Barros, A. L. B. (2016). A robust extreme learning machine for pattern classification with outliers. Neurocomputing, 176, 3–13.

    Google Scholar 

  • Bauder, R. A., & Khoshgoftaar, T. M. (2018). The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health information science and systems, 6(1), 1–14.

    Google Scholar 

  • Bawono, A. H., & Bachtiar, F. A. (2019, September). Outlier Detection with Supervised Learning Method. In 2019 International Conference on Sustainable Information Engineering and Technology (SIET) (pp. 306–309). IEEE.

  • Cai, Z., Gu, J., Luo, J., Zhang, Q., Chen, H., Pan, Z., & Li, C. (2019). Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Systems with Applications, 138, 112814.

    Google Scholar 

  • Cao, L., Zhu, P., Zhao, Y., & Zhao, J. (2018). Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids. Journal of hazardous materials, 352, 17–26.

    Google Scholar 

  • Chacko, B. P., Krishnan, V., Raju, V. R., G., & Anto, B., P (2012). Handwritten character recognition using wavelet energy and extreme learning machine. International Journal of Machine Learning and Cybernetics, 3(2), 149–161.

    Google Scholar 

  • Chakraborty, B., Chaterjee, A., Malakar, S., & Sarkar, R. (2022). An iterative approach to unsupervised outlier detection using ensemble method and distance-based data filtering. Complex & Intelligent Systems, 1–16.

  • Chen, F. L., & Ou, T. Y. (2011). Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), 1336–1345.

    Google Scholar 

  • Chen, L., Zhang, Y. H., Huang, G., Pan, X., Wang, S., Huang, T., & Cai, Y. D. (2018). Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection. Molecular genetics and genomics, 293(1), 137–149.

    Google Scholar 

  • Chen, C., Jin, X., Jiang, B., & Li, L. (2019). Optimizing extreme learning machine via generalized hebbian learning and intrinsic plasticity learning. Neural Processing Letters, 49(3), 1593–1609.

    Google Scholar 

  • Chen, S., Wu, M., Wen, P., Xu, F., Wang, S., & Zhao, S. (2021). A Multimode Anomaly Detection Method based on OC-ELM for Aircraft Engine System. IEEE Access: Practical Innovations, Open Solutions, 9, 28842–28855.

    Google Scholar 

  • Cheng, K., Gao, S., Dong, W., Yang, X., Wang, Q., & Yu, H. (2020). Boosting label weighted extreme learning machine for classifying multi-label imbalanced data. Neurocomputing, 403, 360–370.

    Google Scholar 

  • Cogoljević, D., Alizamir, M., Piljan, I., Piljan, T., Prljić, K., & Zimonjić, S. (2018). A machine learning approach for predicting the relationship between energy resources and economic development. Physica A: Statistical Mechanics and its Applications, 495, 211–214.

    Google Scholar 

  • da Costa, N. L., Llobodanin, L. A. G., de Lima, M. D., Castro, I. A., & Barbosa, R. (2018). Geographical recognition of Syrah wines by combining feature selection with Extreme Learning Machine. Measurement, 120, 92–99.

    Google Scholar 

  • Daliri, M. R. (2012). A hybrid automatic system for the diagnosis of lung cancer based on genetic algorithm and fuzzy extreme learning machines. Journal of medical systems, 36(2), 1001–1005.

    Google Scholar 

  • Dashdondov, K., & Kim, M. H. (2021). Mahalanobis Distance based Multivariate Outlier detection to improve performance of Hypertension Prediction. Neural Processing Letters, 1–13.

  • Dekker, R. (2006). The importance of having data-sets.

  • Devika, K., Mahapatra, D., Subramanian, R., & Oruganti, V. R. M. (2022). Outlier-based Autism detection using Longitudinal Structural MRI. IEEE Access: Practical Innovations, Open Solutions, 10, 27794–27808.

    Google Scholar 

  • Ding, S., Xu, X., & Nie, R. (2014). Extreme learning machine and its applications. Neural Computing and Applications, 25(3), 549–556.

    Google Scholar 

  • Duan, J., Ou, Y., Hu, J., Wang, Z., Jin, S., & Xu, C. (2017). Fast and stable learning of dynamical systems based on extreme learning machine. IEEE Transactions on Systems Man and Cybernetics: Systems, 49(6), 1175–1185.

    Google Scholar 

  • Eshtay, M., Faris, H., & Obeid, N. (2020). A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of Extreme Learning Machines for classification problems. International Journal of Machine Learning and Cybernetics, 11(8), 1801–1823.

    Google Scholar 

  • Feng, G., Huang, G. B., Lin, Q., & Gay, R. (2009). Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks, 20(8), 1352–1357.

    Google Scholar 

  • Feng, G., Qian, Z., & Zhang, X. (2012). Evolutionary selection extreme learning machine optimization for regression. Soft Computing, 16(9), 1485–1491.

    Google Scholar 

  • Fernández, Á., Bella, J., & Dorronsoro, J. R. (2022). Supervised outlier detection for classification and regression. Neurocomputing, 486, 77–92.

    Google Scholar 

  • Frénay, B., & Verleysen, M. (2015). Reinforced extreme learning machines for fast robust regression in the presence of outliers. IEEE Transactions on Cybernetics, 46(12), 3351–3363.

    Google Scholar 

  • Geng, Z., Dong, J., Chen, J., & Han, Y. (2017a). A new self-organizing extreme learning machine soft sensor model and its applications in complicated chemical processes. Engineering Applications of Artificial Intelligence, 62, 38–50.

    Google Scholar 

  • Geng, Z., Zhao, S., Tao, G., & Han, Y. (2017b). Early warning modeling and analysis based on analytic hierarchy process integrated extreme learning machine (AHP-ELM): Application to food safety. Food Control, 78, 33–42.

    Google Scholar 

  • Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access: Practical Innovations, Open Solutions, 7, 36266–36273.

    Google Scholar 

  • Guo, M., Ma, Y., Yang, X., & Mankin, R. W. (2019). Detection of damaged wheat kernels using an impact acoustic signal processing technique based on gaussian modelling and an improved extreme learning machine algorithm. biosystems engineering, 184, 37–44.

    Google Scholar 

  • Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2013). Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and data Engineering, 26(9), 2250–2267.

    Google Scholar 

  • Hashmi, A. S., & Ahmad, T. (2019). GP-ELM-RNN: Garson-pruned extreme learning machine based replicator neural network for anomaly detection. Journal of King Saud University-Computer and Information Sciences, 34, 1768–1774.

    Google Scholar 

  • He, Q., Shang, T., Zhuang, F., & Shi, Z. (2013). Parallel extreme learning machine for regression based on MapReduce. Neurocomputing, 102, 52–58.

    Google Scholar 

  • Heeswijk, M. V., Miche, Y., Lindh-Knuutila, T., Hilbers, P. A., Honkela, T., Oja, E., & Lendasse, A. (2009, September). Adaptive ensemble models of extreme learning machines for time series prediction. In International Conference on Artificial Neural Networks (pp. 305–314). Springer, Berlin, Heidelberg.

  • Horata, P., Chiewchanwattana, S., & Sunat, K. (2013). Robust extreme learning machine. Neurocomputing, 102, 31–44.

    Google Scholar 

  • Hu, X. F., Zhao, Z., Wang, S., Wang, F. L., He, D. K., & Wu, S. K. (2008). Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester. Neural Computing and Applications, 17(4), 399–403.

    Google Scholar 

  • Hu, K., Yang, W., & Gao, X. (2017). Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform. Expert Systems with Applications, 86, 135–144.

    Google Scholar 

  • Huang, G. B., Chen, L., & Siew, C. K. (2006a). Universal approximation using incremental constructive feedforward networks with random hidden nodes. Ieee Transaction On Neural Networks, 17(4), 879–892.

    Google Scholar 

  • Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006b). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501.

    Google Scholar 

  • Huang, G. B., Li, M. B., Chen, L., & Siew, C. K. (2008). Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing, 71(4–6), 576–583.

    Google Scholar 

  • Huang, G. B., Wang, D. H., & Lan, Y. (2011a). Extreme learning machines: A survey. International journal of machine learning and cybernetics, 2(2), 107–122.

    Google Scholar 

  • Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2011b). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 42(2), 513–529.

    Google Scholar 

  • Huang, F., Yin, K., Huang, J., Gui, L., & Wang, P. (2017). Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Engineering Geology, 223, 11–22.

    Google Scholar 

  • Huang, W., Yang, Y., Lin, Z., Huang, G. B., Zhou, J., Duan, Y., & Xiong, W. (2014, August). Random feature subspace ensemble based extreme learning machine for liver tumor detection and segmentation. In 2014 36th annual international conference of the IEEE engineering in medicine and biology society (pp. 4675–4678). IEEE.

  • Huang, W., Tan, Z. M., Lin, Z., Huang, G. B., Zhou, J., Chui, C. K., & Chang, S. (2012, August). A semi-automatic approach to the segmentation of liver parenchyma from 3D CT images with extreme learning machine. In 2012 annual international conference of the IEEE engineering in medicine and biology society (pp. 3752–3755). IEEE.

  • Janakiraman, V. M., & Nielsen, D. (2016, July). Anomaly detection in aviation data using extreme learning machines. In 2016 international joint conference on neural networks (IJCNN) (pp. 1993–2000). IEEE.

  • Jiang, C., Song, J., Liu, G., Zheng, L., & Luan, W. (2018). Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism. IEEE Internet of Things Journal, 5(5), 3637–3647.

    Google Scholar 

  • Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 1–54.

    Google Scholar 

  • Kalid, S. N., Ng, K. H., Tong, G. K., & Khor, K. C. (2020). A multiple classifiers system for anomaly detection in credit card data with unbalanced and overlapped classes. IEEE Access: Practical Innovations, Open Solutions, 8, 28210–28221.

    Google Scholar 

  • Kang, X., Liu, C., Zeng, S., Zhao, Z., Qian, J., & Zhao, Y. (2018). Prediction of Henry’s law constant of CO2 in ionic liquids based on SEP and Sσ-profile molecular descriptors. Journal of Molecular Liquids, 262, 139–147.

    Google Scholar 

  • Karpagachelvi, S., Arthanari, M., & Sivakumar, M. (2012). Classification of electrocardiogram signals with support vector machines and extreme learning machine. Neural Computing and Applications, 21(6), 1331–1339.

    Google Scholar 

  • Kiani, R., Keshavarzi, A., & Bohlouli, M. (2020). Detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks. Applied Artificial Intelligence, 34(5), 345–377.

    Google Scholar 

  • Kiani, R., Montazeri, M., & Minaei-Bidgoli, B. Intelligent Production and Detection Template of Outlier Dataset Using Clustering. In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) (pp. 145–152). IEEE.

  • Kim, J., Shin, H. S., Shin, K., & Lee, M. (2009). Robust algorithm for arrhythmia classification in ECG using extreme learning machine. Biomedical engineering online, 8(1), 1–12.

    Google Scholar 

  • Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221–232.

    Google Scholar 

  • Lama, R. K., Gwak, J., Park, J. S., & Lee, S. W. (2017). Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. Journal of healthcare engineering, 2017, 5485080.

  • Lan, Y., Soh, Y. C., & Huang, G. B. (2009). Ensemble of online sequential extreme learning machine. Neurocomputing, 72(13–15), 3391–3395.

    Google Scholar 

  • Lan, Y., Soh, Y. C., & Huang, G. B. (2010a). Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing, 73(16–18), 3191–3199.

    Google Scholar 

  • Lan, Y., Soh, Y. C., & Huang, G. B. (2010b). Two-stage extreme learning machine for regression. Neurocomputing, 73(16–18), 3028–3038.

    Google Scholar 

  • Lan, Y., Hu, Z., Soh, Y. C., & Huang, G. B. (2013). An extreme learning machine approach for speaker recognition. Neural Computing and Applications, 22(3), 417–425.

    Google Scholar 

  • Landa-Torres, I., Ortiz-Garcia, E. G., Salcedo-Sanz, S., Segovia-Vargas, M. J., Gil-Lopez, S., Miranda, M., & Del Ser, J. (2012). Evaluating the internationalization success of companies through a hybrid grouping harmony search—extreme learning machine approach. IEEE Journal of Selected Topics in Signal Processing, 6(4), 388–398.

    Google Scholar 

  • Lee, Y., Lee, H., Kim, J., Shin, H. C., & Lee, M. (2009). Classification of BMI control commands from rat’s neural signals using extreme learning machine. BioMedical Engineering OnLine, 8(1), 1–10.

    Google Scholar 

  • Lee, K., Lee, C. H., & Lee, J. (2021). Semi-supervised Anomaly Detection Algorithm using probabilistic labeling (SAD-PL). IEEE Access: Practical Innovations, Open Solutions, 9, 142972–142981.

    Google Scholar 

  • Leevy, J. L., Khoshgoftaar, T. M., Bauder, R. A., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1), 1–30.

    Google Scholar 

  • Lei, H., Wen, Y., You, Z., Elazab, A., Tan, E. L., Zhao, Y., & Lei, B. (2018). Protein–protein interactions prediction via multimodal deep polynomial network and regularized extreme learning machine. IEEE journal of biomedical and health informatics, 23(3), 1290–1303.

    Google Scholar 

  • Li, G., & Niu, P. (2013). An enhanced extreme learning machine based on ridge regression for regression. Neural Computing and Applications, 22(3), 803–810.

    Google Scholar 

  • Li, K., Kong, X., Lu, Z., Wenyin, L., & Yin, J. (2014). Boosting weighted ELM for imbalanced learning. Neurocomputing, 128, 15–21.

    Google Scholar 

  • Li, W., Chen, C., Su, H., & Du, Q. (2015). Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3681–3693.

    Google Scholar 

  • Li, J., Shi, X., You, Z. H., Yi, H. C., Chen, Z., Lin, Q., & Fang, M. (2020a). Using weighted extreme learning machine combined with scale-invariant feature transform to predict protein-protein interactions from protein evolutionary information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5), 1546–1554.

    Google Scholar 

  • Li, Q., Song, Y., Zhang, J., & Sheng, V. S. (2020b). Multiclass imbalanced learning with one-versus-one decomposition and spectral clustering. Expert Systems with Applications, 147, 113152.

    Google Scholar 

  • Li, Z., Wei, L., Li, W., Wei, L., Chen, M., Lv, M., & Gao, N. (2019, October). Research on DDoS attack detection based on ELM in IoT environment. In 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS) (pp. 144–148). IEEE.

  • Liang, N. Y., Huang, G. B., Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on neural networks, 17(6), 1411–1423.

    Google Scholar 

  • Ling, C. X., & Sheng, V. S. (2008). Cost-sensitive learning and the class imbalance problem. Encyclopedia of machine learning, 2011, 231–235.

  • Liu, T., Yang, Y., Huang, G. B., Yeo, Y. K., & Lin, Z. (2015). Driver distraction detection using semi-supervised machine learning. IEEE transactions on intelligent transportation systems, 17(4), 1108–1120.

    Google Scholar 

  • Liu, J., Li, Q., Chen, W., Yan, Y., & Wang, X. (2018). A fast fault diagnosis method of the PEMFC system based on extreme learning machine and Dempster–Shafer evidence theory. IEEE Transactions on Transportation Electrification, 5(1), 271–284.

    Google Scholar 

  • Liu, Y., Li, X., Wang, J., Chen, F., Wang, J., Shi, Y., & Zheng, L. (2017, October). Pattern Discovery from Big Data of Food Sampling Inspections Based on Extreme Learning Machine. In International Conference on Research and Practical Issues of Enterprise Information Systems (pp. 132–142). Springer, Cham.

  • Liu, B., Yan, S., You, H., Dong, Y., Li, J., Li, Y., & Gu, R. (2017, December). An ensembled RBF extreme learning machine to forecast road surface temperature. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 977–980). IEEE.

  • Lu, X., Ming, L., Liu, W., & Li, H. X. (2017). Probabilistic regularized extreme learning machine for robust modeling of noise data. IEEE Transactions on Cybernetics, 48(8), 2368–2377.

    Google Scholar 

  • Lu, S., Lu, Z., Yang, J., Yang, M., & Wang, S. (2018). A pathological brain detection system based on kernel based ELM. Multimedia tools and applications, 77(3), 3715–3728.

    Google Scholar 

  • Malar, E., Kandaswamy, A., Chakravarthy, D., & Dharan, A. G. (2012). A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine. Computers in biology and medicine, 42(9), 898–905.

    Google Scholar 

  • Mao, W., Wang, J., & Xue, Z. (2017). An ELM-based model with sparse-weighting strategy for sequential data imbalance problem. International Journal of Machine Learning and Cybernetics, 8(4), 1333–1345.

    Google Scholar 

  • Marjanović, V., Milovančević, M., & Mladenović, I. (2016). Prediction of GDP growth rate based on carbon dioxide (CO2) emissions. Journal of CO2 Utilization, 16, 212–217.

    Google Scholar 

  • Marković, D., Petković, D., Nikolić, V., Milovančević, M., & Petković, B. (2017). Soft computing prediction of economic growth based in science and technology factors. Physica A: Statistical Mechanics and its Applications, 465, 217–220.

    Google Scholar 

  • Mi, X. W., Liu, H., & Li, Y. F. (2017). Wind speed forecasting method using wavelet, extreme learning machine and outlier correction algorithm. Energy Conversion and Management, 151, 709–722.

    Google Scholar 

  • Milačić, L., Jović, S., Vujović, T., & Miljković, J. (2017). Application of artificial neural network with extreme learning machine for economic growth estimation. Physica A: Statistical Mechanics and its Applications, 465, 285–288.

    MathSciNet  Google Scholar 

  • Minhas, R., Baradarani, A., Seifzadeh, S., & Wu, Q. J. (2010). Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing, 73(10–12), 1906–1917.

    Google Scholar 

  • Mohammed, A. A., Minhas, R., Wu, Q. J., & Sid-Ahmed, M. A. (2011). Human face recognition based on multidimensional PCA and extreme learning machine. Pattern recognition, 44(10–11), 2588–2597.

    Google Scholar 

  • Muñoz-Ramírez, V., Kmetzsch, V., Forbes, F., Meoni, S., Moro, E., & Dojat, M. (2022). Subtle anomaly detection: Application to brain MRI analysis of de novo parkinsonian patients. Artificial Intelligence in Medicine, 125, 102251.

    Google Scholar 

  • Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018). Enhanced network anomaly detection based on deep neural networks. IEEE access, 6, 48231–48246.

    Google Scholar 

  • Nayak, D. R., Das, D., Dash, R., Majhi, S., & Majhi, B. (2020). Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images. Multimedia Tools and Applications, 79(21), 15381–15396.

    Google Scholar 

  • Nguyen, D. T., Ryu, S., Qureshi, M. N. I., Choi, M., Lee, K. H., & Lee, B. (2019). Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer’s dementia diagnosis using multi-measure rs-fMRI spatial patterns. PloS one, 14(2), e0212582.

    Google Scholar 

  • Nian, R., He, B., & Lendasse, A. (2013). 3D object recognition based on a geometrical topology model and extreme learning machine. Neural Computing and Applications, 22(3), 427–433.

    Google Scholar 

  • Niu, W. J., Feng, Z. K., Feng, B. F., Min, Y. W., Cheng, C. T., & Zhou, J. Z. (2019). Comparison of multiple linear regression, artificial neural network, extreme learning machine, and support vector machine in deriving operation rule of hydropower reservoir. Water, 11(1), 88.

    Google Scholar 

  • Niu, M., Zhang, J., Li, Y., Wang, C., Liu, Z., Ding, H., & Ma, Q. (2020). CirRNAPL: A web server for the identification of circRNA based on extreme learning machine. Computational and structural biotechnology journal, 18, 834–842.

    Google Scholar 

  • Oikawa, H., Nishida, T., Sakamoto, R., Matsutani, H., & Kondo, M. (2020, September). Fast semi-supervised anomaly detection of drivers’ behavior using online sequential extreme learning machine. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (pp. 1–8). IEEE.

  • Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., & Anguita, D. (2017). Dynamic delay predictions for large-scale railway networks: Deep and shallow extreme learning machines tuned via thresholdout. IEEE Transactions on Systems Man and Cybernetics: Systems, 47(10), 2754–2767.

    Google Scholar 

  • Ouyang, Q., Chen, Q., Zhao, J., & Lin, H. (2013). Determination of amino acid nitrogen in soy sauce using near infrared spectroscopy combined with characteristic variables selection and extreme learning machine. Food and Bioprocess Technology, 6(9), 2486–2493.

    Google Scholar 

  • Ouyang, T., Wang, C., Yu, Z., Stach, R., Mizaikoff, B., Huang, G. B., & Wang, Q. J. (2020). NOx measurements in vehicle exhaust using advanced deep ELM networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.

    Google Scholar 

  • Pan, C., Park, D. S., Lu, H., & Wu, X. (2012a). Color image segmentation by fixation-based active learning with ELM. Soft Computing, 16(9), 1569–1584.

    Google Scholar 

  • Pan, C., Park, D. S., Yang, Y., & Yoo, H. M. (2012b). Leukocyte image segmentation by visual attention and extreme learning machine. Neural Computing and Applications, 21(6), 1217–1227.

    Google Scholar 

  • Paullada, A., Raji, I. D., Bender, E. M., Denton, E., & Hanna, A. (2021). Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns, 2(11), 100336.

    Google Scholar 

  • Peng, X., Lin, P., Zhang, T., & Wang, J. (2013). Extreme learning machine-based classification of ADHD using brain structural MRI data. PloS one, 8(11), e79476.

  • Qin, Y., Li, M., De, G., Huang, L., Yang, S., Tan, Q., & Zhou, F. (2019). Research on green management effect evaluation of power generation enterprises in China based on dynamic hesitation and improved extreme learning machine. Processes, 7(7), 474.

    Google Scholar 

  • Qureshi, M. N. I., Min, B., Jo, H. J., & Lee, B. (2016). Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: Structural MRI study. PloS one, 11(8), e0160697.

  • Qureshi, M. N. I., Oh, J., Cho, D., Jo, H. J., & Lee, B. (2017). Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Frontiers in neuroinformatics, 11, 59.

    Google Scholar 

  • Raghuwanshi, B. S., & Shukla, S. (2021). Classifying imbalanced data using SMOTE based class-specific kernelized ELM. International Journal of Machine Learning and Cybernetics, 12(5), 1255–1280.

    Google Scholar 

  • Rakic, G., Milenkovic, D., Vujovic, S., Vujovic, T., & Jović, S. (2019). Information system for e-GDP based on computational intelligence approach. Physica A: Statistical Mechanics and its Applications, 513, 418–423.

    Google Scholar 

  • Ramalho, G. L. B., Rebouças Filho, P. P., Medeiros, F. N. S. D., & Cortez, P. C. (2014). Lung disease detection using feature extraction and extreme learning machine. Revista Brasileira de Engenharia Biomédica, 30(3), 207–214.

    Google Scholar 

  • Rathore, S., & Park, J. H. (2018). Semi-supervised learning based distributed attack detection framework for IoT. Applied Soft Computing, 72, 79–89.

    Google Scholar 

  • Ri, J. H., Tian, G., Liu, Y., Xu, W. H., & Lou, J. G. (2020). Extreme learning machine with hybrid cost function of G-mean and probability for imbalance learning. International Journal of Machine Learning and Cybernetics, 11(9), 2007–2020.

    Google Scholar 

  • Rong, H. J., Ong, Y. S., Tan, A. H., & Zhu, Z. (2008). A fast pruned-extreme learning machine for classification problem. Neurocomputing, 72(1–3), 359–366.

    Google Scholar 

  • Sahu, S. K., Mohapatra, D. P., & Ray, N. K. (2021, December). An Ensemble-based Outlier Detection Approach on Intrusion Detection. In 2021 19th OITS International Conference on Information Technology (OCIT) (pp. 404–409). IEEE.

  • Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search–Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445–452.

    Google Scholar 

  • Sekar, M. (2022). Fraud and Anomaly Detection. In Machine Learning for Auditors (pp. 193–202). Apress, Berkeley, CA.

  • She, Q., Hu, B., Gan, H., Fan, Y., Nguyen, T., Potter, T., & Zhang, Y. (2018). Safe semi-supervised extreme learning machine for EEG signal classification. IEEE Access: Practical Innovations, Open Solutions, 6, 49399–49407.

    Google Scholar 

  • Shoumo, S. Z. H., Dhruba, M. I. M., Hossain, S., Ghani, N. H., Arif, H., & Islam, S. (2019, October). Application of machine learning in credit risk assessment: a prelude to smart banking. In TENCON 2019–2019 IEEE Region 10 Conference (TENCON) (pp. 2023–2028). IEEE.

  • Shukla, S., & Yadav, R. N. (2015). Regularized weighted circular complex-valued extreme learning machine for imbalanced learning. IEEE Access: Practical Innovations, Open Solutions, 3, 3048–3057.

    Google Scholar 

  • Shukla, A. K., Kumar, S., Jagdev, R., Muhuri, P. K., & Lohani, Q. D. (2018, July). Interval Type-2 Fuzzy weighted Extreme Learning Machine for GDP Prediction. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.

  • Siqi, W., Qiang, L., Xifeng, G., En, Z., & Jianping, Y. (2019). Fast and unsupervised outlier removal by recurrent adaptive reconstruction extreme learning machine. International Journal of Machine Learning and Cybernetics, 10(12), 3539–3556.

    Google Scholar 

  • Sokolov-Mladenović, S., Milovančević, M., Mladenović, I., & Alizamir, M. (2016). Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters. Computers in Human Behavior, 65, 43–45.

    Google Scholar 

  • Song, J. L., Hu, W., & Zhang, R. (2016). Automated detection of epileptic EEGs using a novel fusion feature and extreme learning machine. Neurocomputing, 175, 383–391.

    Google Scholar 

  • Song, T., Li, D., Liu, Z., & Yang, W. (2019). Online ADMM-based extreme learning machine for sparse supervised learning. IEEE Access: Practical Innovations, Open Solutions, 7, 64533–64544.

    Google Scholar 

  • Sridhar, S., & Sanagavarapu, S. (2021, September). ELM-AD: Extreme Learning Machine Framework for Price and Volume Anomaly Detection in Stock Markets. In 2021 International Conference on Computing and Communications Applications and Technologies (I3CAT) (pp. 44–51). IEEE.

  • Su, J., Shen, H., Peng, L., & Hu, D. (2021). Few-shot domain-adaptive anomaly detection for cross-site brain images. IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3125686

  • Sun, W., & Zhang, C. (2018). Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm. Applied energy, 231, 1354–1371.

    Google Scholar 

  • Sun, Z. L., Choi, T. M., Au, K. F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411–419.

    Google Scholar 

  • Sun, Z. L., Ng, K. M., Soszynska-Budny, J., & Habibullah, M. S. (2011). Application of the LP-ELM model on transportation system lifetime optimization. IEEE Transactions on intelligent transportation systems, 12(4), 1484–1494.

    Google Scholar 

  • Sun, W., Wang, C., & Zhang, C. (2017). Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization. Journal of cleaner production, 162, 1095–1101.

    Google Scholar 

  • Sun, C., Yu, Y., Liu, H., & Gu, J. (2015, December). Robotic grasp detection using extreme learning machine. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO) (pp. 1115–1120). IEEE.

  • Tama, B. A., Comuzzi, M., & Rhee, K. H. (2019). TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system. IEEE Access: Practical Innovations, Open Solutions, 7, 94497–94507.

    Google Scholar 

  • Termenon, M., Graña, M., Barrós-Loscertales, A., & Ávila, C. (2013). Extreme learning machines for feature selection and classification of cocaine dependent patients on structural MRI data. Neural processing letters, 38(3), 375–387.

    Google Scholar 

  • Termenon, M., Graña, M., 2, A., Akusok, A., Miche, Y., Björk, K. M., & Lendasse, A. (2016). Brain MRI morphological patterns extraction tool based on Extreme Learning Machine and majority vote classification. Neurocomputing, 174, 344–351.

    Google Scholar 

  • Thammasakorn, C., Chiewchanwattana, S., & Sunat, K. (2018, July). Optimizing weighted elm based on gray wolf optimizer for imbalanced data classification. In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 512–517). IEEE.

  • Van Heeswijk, M., Miche, Y., Oja, E., & Lendasse, A. (2011). GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing, 74(16), 2430–2437.

    Google Scholar 

  • Vani, G., Savitha, R., & Sundararajan, N. (2010, December). Classification of abnormalities in digitized mammograms using extreme learning machine. In 2010 11th International Conference on Control Automation Robotics & Vision (pp. 2114–2117). IEEE.

  • Wang, B., Wang, G., Li, J., & Wang, B. (2012). Update strategy based on region classification using ELM for mobile object index. Soft Computing, 16(9), 1607–1615.

    Google Scholar 

  • Wang, J. N., Jin, J. L., Geng, Y., Sun, S. L., Xu, H. L., Lu, Y. H., & Su, Z. M. (2013). An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes. Journal of computational chemistry, 34(7), 566–575.

    Google Scholar 

  • Wang, Z., Yu, G., Kang, Y., Zhao, Y., & Qu, Q. (2014). Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing, 128, 175–184.

    Google Scholar 

  • Wang, X., Shen, D., Bai, M., Nie, T., Kou, Y., & Yu, G. (2016a). Cluster-based outlier detection using unsupervised extreme learning machines. In Proceedings of ELM-2015 Volume 1 (pp. 135–146). Springer, Cham.

  • Wang, Z., Qu, Q., Yu, G., & Kang, Y. (2016b). Breast tumor detection in double views mammography based on extreme learning machine. Neural Computing and Applications, 27(1), 227–240.

    Google Scholar 

  • Wang, R., Chow, C. Y., Lyu, Y., Lee, V. C., Kwong, S., Li, Y., & Zeng, J. (2017a). TaxiRec: Recommending road clusters to taxi drivers using ranking-based extreme learning machines. IEEE Transactions on Knowledge and Data Engineering, 30(3), 585–598.

    Google Scholar 

  • Wang, X., Bai, M., Shen, D., Nie, T., Kou, Y., & Yu, G. (2017b). A distributed algorithm for the cluster-based outlier detection using unsupervised extreme learning machines. Mathematical Problems in Engineering, 2, 1–12.

    MathSciNet  Google Scholar 

  • Wang, Z., Xin, J., Yang, H., Tian, S., Yu, G., Xu, C., & Yao, Y. (2017c). Distributed and weighted extreme learning machine for imbalanced big data learning. Tsinghua Science and Technology, 22(2), 160–173.

    Google Scholar 

  • Wang, H., Bah, M. J., & Hammad, M. (2019a). Progress in outlier detection techniques: A survey. Ieee Access, 7, 107964–108000.

    Google Scholar 

  • Wang, Z., Li, M., Wang, H., Jiang, H., Yao, Y., Zhang, H., & Xin, J. (2019b). Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access: Practical Innovations, Open Solutions, 7, 105146–105158.

    Google Scholar 

  • Wang, G., Wong, K. W., & Lu, J. (2020a). AUC-based extreme learning machines for supervised and semi-supervised imbalanced classification. IEEE Transactions on Systems Man and Cybernetics: Systems, 51(12), 7919–7930.

    Google Scholar 

  • Wang, Y., Wang, A. N., Ai, Q., & Sun, H. J. (2020b). Enhanced Kernel-Based multilayer fuzzy Weighted Extreme Learning Machines. IEEE Access: Practical Innovations, Open Solutions, 8, 166246–166260.

    Google Scholar 

  • Wang, J., Lu, S., Wang, S. H., & Zhang, Y. D. (2021). A review on extreme learning machine. Multimedia Tools and Applications, 1–50.

  • Wei, X., Liu, H., She, X., Lu, Y., Liu, X., & Mo, S. (2019). Stability Assessment of Rubble Mound Breakwaters using Extreme Learning Machine Models. Journal of Marine Science and Engineering, 7(9), 312.

    Google Scholar 

  • Xia, J., Chen, H., Li, Q., Zhou, M., Chen, L., Cai, Z., & Zhou, H. (2017). Ultrasound-based differentiation of malignant and benign thyroid nodules: An extreme learning machine approach. Computer methods and programs in biomedicine, 147, 37–49.

    Google Scholar 

  • Xie, W., Li, Y., & Ma, Y. (2016). Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing, 173, 930–941.

    Google Scholar 

  • Xing, Y. M., Ban, X. J., & Liu, R. (2018, January). A short-term traffic flow prediction method based on kernel extreme learning machine. In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 533–536). IEEE.

  • Xu, Y., Dai, Y., Dong, Z. Y., Zhang, R., & Meng, K. (2013). Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. Neural Computing and Applications, 22(3), 501–508.

    Google Scholar 

  • Xu, Y., Mo, H., Sun, C., & Luo, F. (2019, July). Imbalanced Learning of Weighted Extreme Learning Machines Ensemble Algorithm in Wastewater Treatment Plant Fault Diagnosis. In 2019 Chinese Control Conference (CCC) (pp. 7528–7533). IEEE.

  • Yan, W. (2016, July). One-class extreme learning machines for gas turbine combustor anomaly detection. In 2016 international joint conference on neural networks (ijcnn) (pp. 2909–2914). IEEE.

  • Yin, Y., Dong, X., & Xu, T. (2018). Rapid and efficient bug assignment using ELM for IOT software. IEEE Access: Practical Innovations, Open Solutions, 6, 52713–52724.

    Google Scholar 

  • You, Z. H., Zhou, M., Luo, X., & Li, S. (2016). Highly efficient framework for predicting interactions between proteins. IEEE transactions on cybernetics, 47(3), 731–743.

    Google Scholar 

  • Yu, H., Sun, C., Yang, X., Zheng, S., Wang, Q., & Xi, X. (2018a). LW-ELM: A fast and flexible cost-sensitive learning framework for classifying imbalanced data. IEEE Access: Practical Innovations, Open Solutions, 6, 28488–28500.

    Google Scholar 

  • Yu, H., Yang, X., Zheng, S., & Sun, C. (2018b). Active learning from imbalanced data: A solution of online weighted extreme learning machine. IEEE transactions on neural networks and learning systems, 30(4), 1088–1103.

    Google Scholar 

  • Yuan, Q., Zhou, W., Li, S., & Cai, D. (2011). Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy research, 96(1–2), 29–38.

    Google Scholar 

  • Zeng, Y., Xu, X., Shen, D., Fang, Y., & Xiao, Z. (2016). Traffic sign recognition using kernel extreme learning machines with deep perceptual features. IEEE Transactions on Intelligent Transportation Systems, 18(6), 1647–1653.

    Google Scholar 

  • Zhai, J., Zhang, S., & Wang, C. (2017). The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers. International Journal of Machine Learning and Cybernetics, 8(3), 1009–1017.

    Google Scholar 

  • Zhang, K., & Luo, M. (2015). Outlier-robust extreme learning machine for regression problems. Neurocomputing, 151, 1519–1527.

    Google Scholar 

  • Zhang, X., & Qin, L. (2022). An improved Extreme Learning Machine for Imbalanced Data classification. IEEE Access: Practical Innovations, Open Solutions, 10, 8634–8642.

    Google Scholar 

  • Zhang, J., Wu, X., & Sheng, V. S. (2014). Imbalanced multiple noisy labeling. IEEE Transactions on Knowledge and Data Engineering, 27(2), 489–503.

    Google Scholar 

  • Zhang, H., Zhang, S., Yin, Y., & Chen, X. (2018a). Prediction of the hot metal silicon content in blast furnace based on extreme learning machine. International Journal of Machine Learning and Cybernetics, 9(10), 1697–1706.

    Google Scholar 

  • Zhang, Y. D., Zhao, G., Sun, J., Wu, X., Wang, Z. H., Liu, H. M., & Li, J. (2018b). Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimedia Tools and Applications, 77(17), 22629–22648.

    Google Scholar 

  • Zhang, D., Peng, X., Pan, K., & Liu, Y. (2019). A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy conversion and management, 180, 338–357.

    Google Scholar 

  • Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020). Robust extreme learning machine for modeling with unknown noise. Journal of the Franklin Institute, 357(14), 9885–9908.

    MathSciNet  Google Scholar 

  • Zhang, X., He, T., Lu, L., Yue, S., Cheng, D., & Xu, X. (2017, November). Video analysis of traffic accidents based on projection extreme learning machine. In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) (pp. 149–154). IEEE.

  • Zhang, Z., Cai, Y., Gong, W., Liu, X., & Cai, Z. (2020, July). Graph Convolutional Extreme Learning Machine. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE.

  • Zheng, W., Qian, Y., & Lu, H. (2013). Text categorization based on regularization extreme learning machine. Neural Computing and Applications, 22(3), 447–456.

    Google Scholar 

  • Zhou, Z. H., Zhao, J. W., & Cao, F. L. (2013). Surface reconstruction based on extreme learning machine. Neural Computing and Applications, 23(2), 283–292.

    Google Scholar 

  • Zhou, C., Huang, S., Xiong, N., Yang, S. H., Li, H., Qin, Y., & Li, X. (2015). Design and analysis of multimodel-based anomaly intrusion detection systems in industrial process automation. IEEE Transactions on Systems Man and Cybernetics: Systems, 45(10), 1345–1360.

    Google Scholar 

  • Zhou, T., Tang, L., Hang, H., & Yang, P. (2018). Classification of epileptic electroencephalograms signal based on improved extreme learning machine. Journal of Medical Imaging and Health Informatics, 8(1), 33–37.

    Google Scholar 

  • Zhu, W., Huang, W., Lin, Z., Yang, Y., Huang, S., & Zhou, J. (2016). Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation. Multimedia Tools and Applications, 75(5), 2815–2837.

    Google Scholar 

  • Zong, W., & Huang, G. B. (2011). Face recognition based on extreme learning machine. Neurocomputing, 74(16), 2541–2551.

    Google Scholar 

Download references

Acknowledgements

Many thanks for the reviewers’ very constructive remarks. We hope the revised manuscript has addressed satisfactorily your concerns.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Department of Computer Science, Texas Tech University, Lubbock, TX, 79409, USA

    Rasoul Kiani & Victor S. Sheng

  2. Department of Computer Science and Engineering, University of North Texas, Dallas, TX, 75201, USA

    Wei Jin

Authors
  1. Rasoul Kiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor S. Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Rasoul Kiani and Dr. Victor S. Sheng had the idea for the article. Rasoul Kiani performed the literature search and data analysis. Rasoul Kiani, Dr. Victor S. Sheng, and Dr. Wei Jin drafted and critically revised the work.

Corresponding author

Correspondence to Victor S. Sheng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Editors: Nuno Moniz, Paula Branco, Luís Torgo, Nathalie Japkowicz, Michal Wozniak, Shuo Wang.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kiani, R., Jin, W. & Sheng, V.S. Survey on extreme learning machines for outlier detection. Mach Learn 113, 5495–5531 (2024). https://doi.org/10.1007/s10994-023-06375-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10994-023-06375-0

Keywords

Search

Navigation