Framework for the Classification of Imbalanced Structured Data Using Under-sampling and Convolutional Neural Network
Pages 1795 - 1809
Abstract
Among machine learning techniques, classification techniques are useful for various business applications, but classification algorithms perform poorly with imbalanced data. In this study, we propose a classification technique with improved binary classification performance on both the minority and majority classes of imbalanced structured data. The proposed framework is composed of three steps. In the first step, a balanced training set is created via under-sampling. Then, each example is converted into an image depicting a line graph. In the last step, a Convolutional Neural Network (CNN) is trained using the images. In the experiments, we selected six datasets from the UCI Repository and applied the proposed framework to them. The proposed model achieved the best receiver operating characteristic (ROC) curve and Balanced Accuracy (BA) on all the datasets and five datasets, respectively. This demonstrates that the combination of under-sampling and CNNs is a viable approach for imbalanced structure data classification.
References
[1]
Abdel-Hamid, O., Deng, L., & Yu, D. (2013) Exploring convolutional neural network structures and optimization techniques for speech recognition. In Interspeech (Vol. 11, pp. 73–5)
[2]
Ando S Classifying imbalanced data in distance-based feature space Knowledge and Information Systems 2016 46 3 707-730
[3]
Awoyemi, J. O., Adetunmbi, A. O., & Oluwadare, S. A. (2017). Credit card fraud detection using machine learning techniques: a comparative analysis. In 2017 International Conference on Computing Networking and Informatics (ICCNI) (pp. 1–9). IEEE
[4]
Balachandran, P. V., Xue, D., Theiler, J., Hogden, J., Gubernatis, J. E., & Lookman, T. (2018). Importance of feature selection in machine learning and adaptive design for materials. In Materials Discovery and Design (pp. 59–79). Springer
[5]
Bang, C., Lee, J., & Rao, R. (2021). The Egyptian protest movement in the twittersphere: an investigation of dual sentiment pathways of communication. International Journal of Information Management, 58.
[6]
Barandela R, Valdovinos RM, and Sánchez JS New applications of ensembles of classifiers Pattern Analysis & Applications 2003 6 3 245-256
[7]
Benfeldt, O., Persson, J. S., & Madsen, S. (2019). Data governance as a collective action problem. Information Systems Frontiers (pp. 1–15). Springer
[8]
Bessi A and Ferrara E Social bots distort the 2016 US presidential election online discussion First Monday 2016 21 11-17
[9]
Beyan C and Fisher R Classifying imbalanced data sets using similarity based hierarchical decomposition Pattern Recognition 2015 48 5 1653-1672
[10]
Braytee, A., Liu, W., & Kennedy, P. (2016). A cost-sensitive learning strategy for feature extraction from imbalanced data. In International Conference on Neural Information Processing (pp. 78–86). Springer
[11]
Breiman L Bagging predictors Machine Learning 1996 24 2 123-140
[12]
Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-Level-Smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 475–482). Springer
[13]
Castro CL and Braga AP Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data IEEE Transactions on Neural Networks and Learning Systems 2013 24 6 888-899
[14]
Chan, K. K., & Misra, S. (1990). Characteristics of the opinion leader: a new dimension. Journal of Advertising, 19(3), 53–60. Taylor & Francis
[15]
Chawla, N. V., Lazarevic, A., Hall, L. O., & Bowyer, K. W. (2003). SMOTEBoost: Improving prediction of the minority class in boosting. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 107–119). Springer
[16]
Chen S, He H, and Garcia EA RAMOBoost: ranked minority oversampling in boosting IEEE Transactions on Neural Networks 2010 21 10 1624-1642
[17]
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794).
[18]
Chen, X., & Wasikowski, M. (2008). Fast: A roc-based feature selection metric for small samples and imbalanced data classification problems. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 124–132). ACM
[19]
Chen ZY, Fan ZP, and Sun M A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data European Journal of Operational Research 2012 223 2 461-472
[20]
Colton D and Hofmann M Sampling techniques to overcome class imbalance in a cyberbullying context Journal of Computer-Assisted Linguistic Research 2019 3 1 21
[21]
D’Addabbo A and Maglietta R Parallel selective sampling method for imbalanced and large data classification Pattern Recognition Letters 2015 62 61-67
[22]
Dastile, X., Celik, T., & Potsane, M. (2020). Statistical and machine learning models in credit scoring: a systematic literature survey. Applied Soft Computing,91, 106263. Elsevier
[23]
Datta, S., & Das, S. (2015). Near-bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs. Neural Networks, 70, 39–52
[24]
Dellarocas C and Wood CA The sound of silence in online feedback: estimating trading risks in the presence of reporting bias Management Science 2008 54 3460-3476
[25]
Díez-Pastor JF, Rodríguez JJ, García-Osorio C, and Kuncheva LI Random balance: ensembles of variable priors classifiers for imbalanced data Knowledge-Based Systems 2015 85 96-111
[26]
Drummond, C., & Holte, R. C. (2003). C4. 5, Class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In Workshop on Learning from Imbalanced Datasets II (Vol. 11, pp. 1–8). Citeseer
[27]
Dullaghan, C., & Rozaki, E. (2017). Integration of machine learning techniques to evaluate dynamic customer segmentation analysis for mobile customers. ArXiv Preprint ArXiv:1702.02215
[28]
Dwivedi, Y. K., Kelly, G., Janssen, M., Rana, N. P., Slade, E. L., & Clement, M. (2018). Social media: the good, the bad, and the ugly. Information Systems Frontiers,20(3), 419–423. Springer
[29]
Ezenkwu, C. P., Ozuomba, S., & Kalu, C. (2015). Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services. Citeseer
[30]
Fawcett T An introduction to ROC analysis Pattern Recognition Letters 2006 27 8 861-874
[31]
Fertier, A., Barthe-Delanoë, A. M., Montarnal, A., Truptil, S., & Bénaben, F. (2020). A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time,. Decision Support Systems, 133, 113260. Elsevier
[32]
Freund Y, Schapire R, and Abe N A short introduction to boosting Journal-Japanese Society For Artificial Intelligence 1999 14 771-7801612
[33]
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences,55(1), 119–139. Elsevier
[34]
Galar M, Fernández A, Barrenechea E, and Herrera F EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling Pattern Recognition 2013 (46 12 3460-3471
[35]
Gao, X., Chen, Z., Tang, S., Zhang, Y., & Li, J. (2016). Adaptive weighted imbalance learning with application to abnormal activity recognition. Neurocomputing,173, 1927–1935
[36]
García, V., Sánchez, J. S., Rodríguez-Picón, L. A., Méndez-González, L. C., & de Jesús Ochoa-Domínguez, H. (2019). Using regression models for predicting the product quality in a tubing extrusion process. Journal of Intelligent Manufacturing,30(6), 2535–2544. Springer
[37]
García-Pedrajas N and García-Osorio C Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections Progress in Artificial Intelligence 2013 2 1 29-44
[38]
Geller, J., Scherl, R., & Perl, Y. (2002). Mining the web for target marketing information. Proceedings of CollECTeR, Toulouse, France
[39]
Ghazikhani, A., Monsefi, R., & Yazdi, H. S. (2013). Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing,122, 535–544
[40]
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In 2014 IEEE Conference on Computer Vision and PatternRecognition (pp. 580–587). IEEE.
[41]
Guo, C., & Berkhahn, F. (2016). Entity embeddings of categorical variables. ArXiv Preprint ArXiv:1604.06737
[42]
Guo, X., Yin, Y., Dong, C., Yang, G., & Zhou, G. (2008). On the class imbalance problem. In 2008 Fourth International Conference on Natural Computation (pp. 192–201). IEEE.
[43]
Gupta, Y. (2018). Selection of important features and predicting wine quality using machine learning techniques. Procedia Computer Science,125, 305–312. Elsevier
[44]
Ha, J., & Lee, J. S. (2016). A new under-sampling method using genetic algorithm for imbalanced data classification. In Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication (p. 95). ACM
[45]
Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, and Bing G Learning from class-imbalanced data: review of methods and applications Expert Systems with Applications 2017 73 220-239
[46]
Han, H., Wang, W. Y., & Mao, B. H. (2005). Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing (pp. 878–887). Springer
[47]
He, H., Bai, Y., Garcia, E. A., & Li, S. (2008). ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1322–1328). IEEE
[48]
Hosseini, H., Xiao, B., Jaiswal, M., & Poovendran, R. (2017). On the limitation of convolutional neural networks in recognizing negative images. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 352–358). IEEE
[49]
Hu, S., Liang, Y., Ma, L., & He, Y. (2009). MSMOTE: Improving classification performance when training data is imbalanced. In Computer Science and Engineering, 2009. WCSE’09. Second International Workshop On (Vol. 2, pp. 13–17). IEEE
[50]
Huang, C. K., Wang, T., & Huang, T. Y. (2020). Initial evidence on the impact of big data implementation on firm performance. Information Systems Frontiers,22(2), 475–487. Springer
[51]
Ioffe, S., & Szegedy, C. (2015). Batch normalization: accelerating deep network training by reducing internal covariate shift. ArXiv Preprint ArXiv:1502.03167
[52]
Japkowicz N and Stephen S The class imbalance problem: a systematic study Intelligent Data Analysis 2002 6 5 429-449
[53]
Jing, L., Zhao, M., Li, P., & Xu, X. (2017). A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement, 111, 1–10
[54]
Johnson, J. M., & Khoshgoftaar, T. M. (2020). The effects of data sampling with deep learning and highly imbalanced big data. Information Systems Frontiers,22(5), 1113–1131. Springer
[55]
Kansal, T., Bahuguna, S., Singh, V., & Choudhury, T. (2018). Customer segmentation using K-Means clustering. In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) (pp. 135–139). IEEE
[56]
Kim S, Kim H, and Namkoong Y Ordinal classification of imbalanced data with application in emergency and disaster information services IEEE Intelligent Systems 2016 31 5 50-56
[57]
Kizgin, H., Jamal, A., Dey, B. L., & Rana, N. P. (2018). The impact of social media on consumers’ acculturation and purchase intentions. Information Systems Frontiers,20(3), 503–514. Springer
[58]
Kuko M and Pourhomayoun M Single and clustered cervical cell classification with ensemble and deep learning methods Information Systems Frontiers 2020 (22 5 1039-1051
[59]
Lane PC, Clarke D, and Hender P On developing robust models for favourability analysis: model choice, feature sets and imbalanced data Decision Support Systems 2012 (53 4 712-718
[60]
LeCun Y, Bengio Y, and Hinton G Deep learning Nature 2015 521 7553436
[61]
LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y. (1999). Object recognition with gradient-based learning. In Shape, Contour and Grouping in Computer Vision (pp. 319–345). Springer
[62]
Li Q, Yang B, Li Y, Deng N, and Jing L Constructing support vector machine ensemble with segmentation for imbalanced datasets Neural Computing and Applications 2013 22 1 249-256
[63]
Li, Z., Kamnitsas, K., & Glocker, B. (2019). Overfitting of neural nets under class imbalance: analysis and improvements for segmentation. ArXiv:1907.10982 [Cs, Stat]. http://arxiv.org/abs/1907.10982
[64]
Liang, J., Bai, L., Dang, C., & Cao, F. (2012). The K-Means-Type algorithms versus imbalanced data distributions. IEEE Transactions on Fuzzy Systems,20(4), 728–745
[65]
Lin WC, Tsai CF, Hu YH, and Jhang JS Clustering-based undersampling in class-imbalanced data Information Sciences 2017 409 17-26
[66]
Liu B and Tsoumakas G Dealing with class imbalance in classifier chains via random undersampling Knowledge-Based Systems 2020 192 105292
[67]
Liu J, Timsina P, and El-Gayar O A comparative analysis of semi-supervised learning: the case of article selection for medical systematic reviews Information Systems Frontiers 2018 20 2 195-207
[68]
Liu XY, Wu J, and Zhou ZH Exploratory undersampling for class-imbalance learning IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 2009 39 2 539-550
[69]
López V, Río D, Benítez S, and Herrera F Cost-sensitive linguistic fuzzy rule based classification systems under the mapreduce framework for imbalanced big data Fuzzy Sets and Systems 2015 258 5-38
[70]
Loyola-González, O., Martínez-Trinidad, J. F., Carrasco-Ochoa, J. A., & García-Borroto, M. (2016). Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases. Neurocomputing,175, 935–947
[71]
Lu, J., Zhang, C., & Shi, F. (2016). A classification method of imbalanced data base on PSO algorithm. In International Conference of Pioneering Computer Scientists, Engineers and Educators (pp. 121–134). Springer
[72]
Maldonado S and López J Imbalanced data classification using second-order cone programming support vector machines Pattern Recognition 2014 47 5 2070-2079
[73]
Mäntymäki, M., Hyrynsalmi, S., & Koskenvoima, A. (2020). How do small and medium-sized game companies use analytics? An attention-based view of game analytics. Information Systems Frontiers,22(5), 1163–1178. Springer
[74]
Mao, W., Wang, J., He, L., & Tian, Y. (2016). two-stage hybrid extreme learning machine for sequential imbalanced data. In Proceedings of ELM-2015 (Vol. 1, pp. 423–433). Springer
[75]
Maratea A, Petrosino A, and Manzo M Adjusted F-Measure and Kernel scaling for imbalanced data learning Information Sciences 2014 257 331-341
[76]
Moepya, S. O., Akhoury, S. S., & Nelwamondo, F. V. (2014). Applying cost-sensitive classification for financial fraud detection under high class-imbalance. In 2014 IEEE International Conference on Data Mining Workshop (pp.183–192). IEEE
[77]
Moreo, A., Esuli, A., & Sebastiani, F. (2016). Distributional random oversampling for imbalanced text classification. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp.805–808). ACM
[78]
Moscato, V., Picariello, A., & Sperlí, G. (2021). A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications, 165, 113986.
[79]
Mustafaraj, E., Finn, S., Whitlock, C., & Metaxas, P. T. (2011). Vocal minority versus silent majority: discovering the opionions of the long tail. In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing (pp. 103–110). IEEE
[80]
Nekooeimehr I and Lai-Yuen SK Adaptive Semi-Unsupervised Weighted Oversampling (A-SUWO) for imbalanced datasets Expert Systems with Applications 2016 46 405-416
[81]
Oh S, Lee MS, and Zhang BT Ensemble learning with active example selection for imbalanced biomedical data classification IEEE/ACM Transactions on Computational Biology and Bioinformatics 2010 8 2 316-325
[82]
Ozan, Å. (2018). A case study on customer segmentation by using machine learning methods. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) (pp. 1–6). IEEE
[83]
Perlich C, Dalessandro B, Raeder T, Stitelman O, and Provost F Machine learning for targeted display advertising: transfer learning in action Machine Learning 2014 95 1
[84]
Powers, D. (2011). Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation. International Journal of Machine Learning Technology (2:1), pp 37–63
[85]
Quinlan, J. R. (2014). C4. 5: Programs for Machine Learning. Elsevier
[86]
Rahman, M. M., & Davis, D. N. (2013). Addressing the class imbalance problem in medical datasets. International Journal of Machine Learning and Computing, 224–228.
[87]
Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization?. In Advances in Neural Information Processing Systems (pp. 2483–2493)
[88]
Seiffert C, Khoshgoftaar TM, Van Hulse J, and Napolitano A RUSBoost: a hybrid approach to alleviating class imbalance IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 2010 40 1 185-197
[89]
Shao YH, Chen WJ, Zhang JJ, Wang Z, and Deng NY An efficient weighted Lagrangian twin support vector machine for imbalanced data classification Pattern Recognition 2014 47 9 3158-3167
[90]
Sharma, S., Bellinger, C., Krawczyk, B., Zaiane, O., & Japkowicz, N. (2018). Synthetic oversampling with the majority class: a new perspective on handling extreme imbalance, In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 447–456). IEEE.
[91]
Smiti, S., & Soui, M. (2020). Bankruptcy prediction using deep learning approach based on borderline SMOTE. Information Systems Frontiers,22(5), 1067–1083. Springer
[92]
Sokolova, M., Japkowicz, N., & Szpakowicz, S. (2006). Beyond accuracy, F-Score and ROC: a family of discriminant measures for performance evaluation. In Australasian Joint Conference on Artificial Intelligence (pp. 1015–1021). Springer
[93]
Sokolova M and Lapalme G A systematic analysis of performance measures for classification tasks Information Processing & Management 2009 (45 4 427-437
[94]
Song L, Hou Y, and Cai Z Recovery-based error estimator for stabilized finite element methods for the stokes equation Computer Methods in Applied Mechanics and Engineering 2014 272 1-16
[95]
Straube, S., & Krell, M. M. (2014). How to evaluate an agent’s behavior to infrequent events?—Reliable performance estimation insensitive to class distribution. Frontiers in Computational Neuroscience, 8, 43
[96]
Sun Y, Kamel MS, Wong AK, and Wang Y Cost-sensitive boosting for classification of imbalanced data Pattern Recognition 2007 40 12 3358-3378
[97]
Sun Z, Song Q, Zhu X, Sun H, Xu B, and Zhou Y A novel ensemble method for classifying imbalanced data Pattern Recognition 2015 48 5 1623-1637
[98]
Sundarkumar GG and Ravi V A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance Engineering Applications of Artificial Intelligence 2015 37 368-377
[99]
Tahir MA, Kittler J, and Yan F Inverse random under sampling for class imbalance problem and its application to multi-label classification Pattern Recognition 2012 45 10 3738-3750
[100]
Tian, H., Chen, S. C., & Shyu, M. L. (2020). Evolutionary programming based deep learning feature selection and network construction for visual data classification. Information Systems Frontiers,22(5), 1053–1066. Springer
[101]
Timsina, P., Liu, J., & El-Gayar, O. (2016). Advanced analytics for the automation of medical systematic reviews. Information Systems Frontiers,18(2), 237–252. Springer
[102]
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015). Efficient object localization using convolutional networks. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June, pp. 648–656). IEEE.
[103]
Tsai, C. F., Lin, W. C., Hu, Y. H., & Yao, G. T. (2019). Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Information Sciences, 477, 47–54
[104]
Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., & Anderla, A. (2019). Credit card fraud detection-machine learning methods. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1–5). IEEE
[105]
Vong CM, Ip WF, Chiu CC, and Wong PK Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine Cognitive Computation 2015 7 3 381-391
[106]
Wang, G., Ledwoch, A., Hasani, R. M., Grosu, R., & Brintrup, A. (2019). A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing,85, 105683. Elsevier
[107]
Wang, S., & Yao, X. (2009). Diversity analysis on imbalanced data sets by using ensemble models. In Computational Intelligence and Data Mining, 2009. CIDM’09. IEEE Symposium On (pp. 324–331). IEEE
[108]
Wu, D., Wang, Z., Chen, Y., & Zhao, H. (2016). Mixed-kernel based weighted extreme learning machine for inertial sensor based human activity recognition with imbalanced dataset. Neurocomputing, 190, 35–49
[109]
Xu Y, Yang Z, Zhang Y, Pan X, and Wang L A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification Knowledge-Based Systems 2016 95 75-85
[110]
Yijing L, Haixiang G, Xiao L, Yanan L, and Jinling L Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data Knowledge-Based Systems 2016 94 88-104
[111]
Zhang, C., Gao, W., Song, J., & Jiang, J. (2016). An imbalanced data classification algorithm of improved autoencoder neural network. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) (pp. 95–99). IEEE
[112]
Zhang Y, Fu P, Liu W, and Chen G Imbalanced data classification based on scaling kernel-based support vector machine Neural Computing and Applications 2014 25 3-4927
[113]
Zhou L Performance of corporate bankruptcy prediction models on imbalanced dataset: the effect of sampling methods Knowledge-Based Systems 2013 41 16-25
[114]
Zolbanin, H. M., Delen, D., Crosby, D., & Wright, D. (2019). A predictive analytics-based decision support system for drug courts. Information Systems Frontiers, 1–20. Springer
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Published: 17 September 2021
Accepted: 26 August 2021
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