Performance Assessment of Learning Algorithms on Multi-Domain Data Sets
Pages 27 - 41
Abstract
This article describes how for the last few decades, data mining research has had significant progress in a wide spectrum of applications. Research in prediction of multi-domain data sets is a challenging task due to the imbalanced, voluminous, conflicting, and complex nature of data sets. A learning algorithm is the most important technique for solving these problems. The learning algorithms are widely used for classification purposes. But choosing the learners that perform best for data sets of particular domains is a challenging task in data mining. This article provides a comparative performance assessment of various state-of-the-art learning algorithms over multi-domain data sets to search the effective classifiers for a particular domain, e.g., artificial, natural, semi-natural, etc. In the present article, a total of 14 real world data sets are selected from University of California, Irvine UCI machine learning repository for conducting experiments using three competent individual learners and their hybrid combinations.
References
[1]
Azar, A. T., & El-Metwally, S. M. 2013. Decision Tree classifiers for automated medical diagnoses. Neural Computing & Applications, 237, 2387-2403.
[2]
BonchiF.GiannottiF.MancoG.RensoC.NanniM.PedreschiD.RuggieriS. 2001. Data Mining for Intelligent Web Caching. In Proceedings of International Conference on Information Technology: Coding and computing pp. 599-603.
[3]
Boutsinas, B., & Vrahatis, M. N. 2001. Artificial Non monotonic Neural Networks ANNNs. Artificial Intelligence, 1321, 1-38.
[4]
Brown, D. E. 2008. Introduction to Data Mining for Medical Informatics. Clinics in Laboratory Medicine, 281, 9-35. 18194716
[5]
ChanP. K.StolfoS. J. 1995. A comparative evaluation of voting and meta-learning on partitioned data. In Proceedings of the 12th International Conference on Machine Learning, San Francisco, CA pp. 90-98.
[6]
ChenM.ZhengA.LloydJ.JordanM.BrewerE. 2004. Failure diagnosis using decision trees. In Proceedings of the International Conference on Autonomic Computing.
[7]
Chen, Y., Li, Z., Nie, L., Hu, X., Wang, X., Chua, T., & Zhang, X. 2012. Semi-Supervised Bayesian Network Model for Microblog Topic Classification. In Proceedings of COLING 2012 pp. 561-576.
[8]
CherkauerK. J. 1996. Human Expert Level Performance on a Scientific Image Analysis Task by a System Using Combined Artificial Neural Networks. In Proc. of the AAAI-96 Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms, Portland, pp. 15-21.
[9]
Cohen, W. W. 1995. Fast Effective Rule Induction, In Proceedings of Twelfth International Conference on Machine Learning, Lake Tahoe, CA.
[10]
DumaM.TwalaB.MarwalaT.NelwamondoF. V. 2011. Improving the Performance of the RIPPER in Insurance Risk Classification: A Comparative Study using feature selection. In Proceedings of 8th International Conference on Informatics in Control, Automation and Robotics pp. 203-210.
[11]
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. 1996. From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining pp. 1-36. Cambridge, Mass.: MIT Press.
[12]
FurnkranzJ.WidmerG. 1994. Incremental reduced error pruning. In Proceedings of the 11th International Conference on Machine Learning ML-94 pp. 70-77.
[13]
Gama, J. 1999. Combining classification algorithms {PhD Thesis}. University of Porto, Portugal.
[14]
Gonzalez, J. P., & Ozguner, U. 2000. Lane detection using histogram-based segmentation and decision trees. In IEEE Intelligent Transportation Systems Conference Proceedings, Dearborn, MI.
[15]
Gross, D. P., Zhang, J., Steenstra, I., Barnsley, S., Haws, C., Amell, T., & Zaiane, O. et al. 2013. Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers. Journal of Occupational Rehabilitation, 234, 597-609. 23468410
[16]
Grzymala-Busse, J. W. 1992. LERS- a system for learning from examples based on rough sets. In Handbook of Applications and Advances of the Rough Sets Theory pp. 3-18. Netherlands: Kluwer.
[17]
Han, J., & Kamber, M. 2006. Data Mining Concepts and Techniques, San Francisco 2nd ed. CA: Elsevier Inc.
[18]
Hastie, T., & Tibishirani, R. 1998. Classification by pair wise coupling. In Advances in Neural Information Processing Systems pp. 507-513. Cambridge: MIT Press.
[19]
Hickey, S. J. 2013. Naive Bayes Classification of Public Health Data with Greedy Feature Selection. Communications of the IIMA, 132, 87-97.
[20]
Jelonek, J., & Stefanowski, J. 1998. Experiments on solving multi-class learning problems by the n2 classifier. In Proceedings of 10th European Conference on Machine Learning ECML-98, LNAI Vol. 1398, pp. 172-177. Springer Verlag.
[21]
Karlik, B., & Oztoprak, E. 2012. Personalized Cancer Treatment by using Naïve Bayes Classifier. International Journal of Machine Learning and Computing, 23, 339-344.
[22]
Kazmierska, J., & Malicki, J. 2008. Application of the Naïve Bayesian Classifier to optimize treatment decisions. Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology, 862, 211-216. 18022719
[23]
Klosgen, W & Z'ytkow, J.M. 2002. Handbook of Data Mining and Knowledge Discovery. Oxford: Oxford University Press.
[24]
Liu, X., Lu, R., Ma, J., Chen, L., & Qin, B. 2016. Privacy-Preserving Patient- Centric Clinical Decision Support System on Naïve Bayesian Classification. IEEE Journal of Biomedical and Health Informatics, 202, 655-668. 26960216
[25]
Macarthur, S. D., Carla, E. B., Avinash, C. K., & Broderick, L. S. 2002. Interactive content-based image retrieval using relevance feedback. Computer Vision and Image Understanding, 882, 55-75.
[26]
Merz, C. 1998. Combining Classifiers Using Correspondence Analysis. Advances in Neural Information Processing Systems, 36, 33-58.
[27]
Murakami, Y., & Mizuguchi, K. 2010. Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites. Bioinformatics, 2615, 1841-1848. 20529890
[28]
Otero, F. E. B., Freitas, A. F., & Johnson, C. G. 2013. A New Sequential Covering Strategy for Inducing classification rules with Ant Colony Algorithms. IEEE Transactions on Evolutionary Computation, 171, 64-76.
[29]
Park, K., Lee, K. M., & Lee, S. 1999. Perceptual grouping of 3D features in aerial image using decision tree classifier. In Proceedings of the International Conference on Image Processing Vol. 1, pp. 31-35.
[30]
Quinlan, J. R. 1986. Introduction to Decision Trees. Journal of Machine Learning, 11, 81-106.
[31]
Quinlan, J. R. 1992. C4.5: Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.
[32]
Rajavignesh, R., Tholkappia, A. G., & Vidivelli, S. 2014. Agent-Based Decision Support System using improved k-NN classifier for effective Diagnosis of Heart Disease. Australian Journal of Basic and Applied Sciences, 817, 160.
[33]
RishI. 2001. An empirical study of the naive Bayes classifier. In Proceedings of IJCAI-01, Workshop on Empirical Methods in Artificial Intelligence.
[34]
Sarkar, B. K., & Sana, S. S. 2009. A hybrid approach to design efficient learning classifiers. Computers & Mathematics with Applications Oxford, England, 581, 65-73.
[35]
Sarkar, B. K., Sana, S. S., & Chaudhuri, K. S. 2010. Accuracy Based Learning Classification System. International Journal of Information and Decision Sciences, 21, 68-86.
[36]
Sarkar, B. K., Sana, S. S., & Chaudhuri, K. S. 2011. Selecting Informative rules with Parallel Genetic Algorithm in Classification Problem. Applied Mathematics and Computation, 2187, 3247-3264.
[37]
Sarkar, B. K., Sana, S. S., & Chaudhuri, K. S. 2011. Minimum Information Loss MIL: A Data Discretization Approach, International Journal of Data Mining. Modelling and Management, 33, 303-318.
[38]
SeeratB.QamarU. 2015. Rule Induction using Enhanced RIPPER Algorithm for Clinical Decision Support System. In Proceedings of the Sixth International Conference on Intelligent Control and Information Processing, Wuhan, China pp. 83-91. IEEE.
[39]
Stefanowski, J. 2001. Algorithms of rule induction for knowledge discovery {Thesis}. Poznan University of Technology.
[40]
StefanowskiJ. 2004. The bagging and n2 classifiers based on rules induced by MODLEM. In Proceedings of the Fourth Int. Conference on Rough Sets and Current Trends in Computing, RSCTC'2004 pp. 488-497. Springer Verlag.
[41]
Stefanowski, J. 2007. On combined classifiers, rule induction and rough sets. In Transactions on Rough Sets 6, LNCS Vol. 4374, 329-350. Springer.
[42]
TanwaniA.FarooqM. 2009. The Role of Biomedical Dataset in Classification. In Proceedings of AMIE: 12th International Conference on Artificial Intelligence pp. 370-374. Springer.
- Performance Assessment of Learning Algorithms on Multi-Domain Data Sets
Recommendations
Evaluating Diagnostic Performance of Machine Learning Algorithms on Breast Cancer
IScIDE 2015: Revised Selected Papers, Part II, of the 5th International Conference on Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques - Volume 9243This paper focuses on comparing performance of six data mining methods namely: Bagging, SVM SMO, Decorate, C4.5 J48, Naïve Bayes and IBK in analyzing Wisconsin Breast Cancer WBC datasets. The datasets were obtained from the UCI Machine Learning ...
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Data mining techniques have been applied to solve classification problems for a variety of applications such as credit scoring, bankruptcy prediction, insurance underwriting, and management fraud detection. In many of those application domains, there ...
Mining of classification patterns in clinical data through data mining algorithms
ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and InformaticsData mining on clinical data is a challenging area in the field of medical research, aiming at predicting and discovering patterns of disease occurrence and prognosis based on detected symptoms and reported health conditions. Data mining is the process ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
IGI Global
United States
Publication History
Published: 01 January 2018
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 17 Dec 2024