Classification with class imbalance problem: A review

International Journal of Advances in Soft Computing and its Applications

Volumn 7, Issue 3, 2015, Pages 176-204

  Ali, Aida ^a   Shamsuddin, Siti Mariyam ^a   Ralescu, Anca L ^b  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b UNIVERSITY OF CINCINNATI   (United States)

Author keywords

Big data; Class imbalance problem; Imbalanced classification; Imbalanced data sets

Indexed keywords

EID: 84949786970     PISSN: 20748523     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (154)

1. Kotsiantis, S., D. Kanellopoulos, and P. Pintelas, Handling imbalanced datasets: a review. GESTS International Transactions on Computer Science and Engineering, 2006. Vol 30(No 1): p. 25-36.
2. Yang, Z., et al. Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2009. 39(6): p. 597-610.
3. Zhu, Z.-B. and Z.-H. Song Fault diagnosis based on imbalance modified kernel Fisher discriminant analysis. Chemical Engineering Research and Design, 2010. 88(8): p. 936-951.
4. Tavallaee, M., N. Stakhanova, and A.A. Ghorbani Toward credible evaluation of anomalybased intrusion-detection methods. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2010. 40(5): p. 516-524.
5. Mazurowski, M.A., et al. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural networks: the official journal of the International Neural Network Society, 2008. 21(2-3): p. 427-436.
6. Soler, V., et al. Imbalanced Datasets Classification by Fuzzy Rule Extraction and Genetic Algorithms. in Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on. 2006.
7. Kubat, M. and S. Matwin. Addressing the curse of imbalanced training sets: one-sided selection. in ICML. 1997.
8. Yi-Hung, L. and C. Yen-Ting. Total margin based adaptive fuzzy support vector machines for multiview face recognition. in Systems, Man and Cybernetics, 2005 IEEE International Conference on. 2005.
9. Li, Y., G. Sun, and Y. Zhu. Data imbalance problem in text classification. in Information Processing (ISIP), 2010 Third International Symposium on. 2010. IEEE.
10. Al-Shahib, A., R. Breitling, and D. Gilbert, Feature selection and the class imbalance problem in predicting protein function from sequence. Applied Bioinformatics, 2005. 4(3): p. 195-203.
11. Japkowicz, N. in Proc AAAI 2000 Workshop on Learning from Imbalanced Data Sets. 2000. AAAI Tech Report WS-00-05.
12. Chawla, N.V., N. Japkowicz, and A. Kotcz. in Proc ICML 2003 Workshop on Learning from Imbalanced Data Sets. 2003.
13. Galar, M., et al. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 2012. 42(4): p. 463-484.
14. Japkowicz, N. Class Imbalance: Are We Focusing on the Right Issue? in Notes from the ICML Workshop on Learning from Imbalanced Data Sets II. 2003.
15. Chawla, N.V. Data mining for imbalanced datasets: An overview, in Data mining and knowledge discovery handbook2005, Springer. p. 853-867.
16. Batista, G.E., R.C. Prati, and M.C. Monard, Balancing strategies and class overlapping, in Advances in Intelligent Data Analysis VI2005, Springer. p. 24-35.
17. Visa, S., Ralescu, A. Issues in mining imbalanced data sets - a review paper. in Proceedings of the Midwest Artificial Intelligence and Cognitive Science Conference. 2005. Dayton.
18. He, H. and E.A. Garcia, Learning from imbalanced data. Knowledge and Data Engineering, IEEE Transactions on, 2009. 21(9): p. 1263-1284.
19. Vaishali, G. An Overview Of Classification Algorithms For Imbalanced Datasets. Int Journal of Emerging technology and Advanced Engineering, 2012. 2(4).
20. Visa, S. Fuzzy Classifiers for Imbalanced Data Sets, in Department of Electrical and Computer Engineering and Computer Science2006, Univeristy of Cincinnati: Cincinnati.
21. Nguyen, G.H., A. Bouzerdoum, and S.L. Phung, Learning Pattern Classification Tasks with Imbalanced Data Sets. 2009.
22. Japkowicz, N. The class imbalance problem: Significance and strategies. in Proceedings of the 2000 International Conference on Artificial Intelligence (ICAI'2000). 2000. Citeseer.
23. Guo, X., et al. On the class imbalance problem. in Natural Computation, 2008. ICNC'08. Fourth International Conference on. 2008. IEEE.
24. Japkowicz, N. Learning from imbalanced data sets: a comparison of various strategies. in AAAI workshop on learning from imbalanced data sets. 2000.
25. Almuallim, H. and T.G. Dietterich. Learning with Many Irrelevant Features. in AAAI. 1991.
26. Dash, M. and H. Liu, Feature selection for classification. Intelligent data analysis, 1997. 1(3): p. 131-156.
27. Yin, L., et al. Feature selection for high-dimensional imbalanced data. Neurocomputing, 2013. 105(0): p. 3-11.
28. Van Hulse, J., et al. Feature selection with high-dimensional imbalanced data. in Data Mining Workshops, 2009. ICDMW'09. IEEE International Conference on. 2009. IEEE.
29. Saeys, Y., I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics. Bioinformatics, 2007. 23(19): p. 2507-2517.
30. John, G.H., R. Kohavi, and K. Pfleger. Irrelevant Features and the Subset Selection Problem. in ICML. 1994.
31. Guyon, I. and A. Elisseeff, An introduction to variable and feature selection. The Journal of Machine Learning Research, 2003. 3: p. 1157-1182.
32. Weiss, G.M. Mining with rarity: a unifying framework. ACM SIGKDD Explorations Newsletter, 2004. 6(1): p. 7-19.
33. Sun, Y., A.K. Wong, and M.S. Kamel, Classification of imbalanced data: A review. International Journal of Pattern Recognition and Artificial Intelligence, 2009. 23(04): p. 687-719.
34. Chawla, N.V., N. Japkowicz, and A. Kotcz, Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl., 2004. 6(1): p. 1-6.
35. Weiss, G.M. and F. Provost, Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research, 2003. 19: p. 315-354.
36. Visa, S., Ralescu, A. The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study. Fuzzy Systems 2005 FUZZ05 The 14th IEEE International Conference on, 749-754, 2005.
37. Japkowicz, N. and S. Stephen, The class imbalance problem: A systematic study. Intelligent data analysis, 2002. 6(5): p. 429-449.
38. Denil, M. and T. Trappenberg, Overlap versus imbalance, in Advances in Artificial Intelligence2010, Springer. p. 220-231.
39. Prati, R.C., G.E. Batista, and M.C. Monard, Class imbalances versus class overlapping: an analysis of a learning system behavior, in MICAI 2004: Advances in Artificial Intelligence2004, Springer. p. 312-321.
40. García, V., et al. Combined Effects of Class Imbalance and Class Overlap on Instance-Based Classification, in Intelligent Data Engineering and Automated Learning - IDEAL 2006, E. Corchado, et al., Editors. 2006, Springer Berlin Heidelberg. p. 371-378.
41. García, V., et al. When overlapping unexpectedly alters the class imbalance effects, in Pattern Recognition and Image Analysis2007, Springer. p. 499-506.
42. García, V., J. Sánchez, and R. Mollineda, An empirical study of the behavior of classifiers on imbalanced and overlapped data sets, in Progress in Pattern Recognition, Image Analysis and Applications2007, Springer. p. 397-406.
43. García, V., R.A. Mollineda, and J.S. Sánchez, On the k-NN performance in a challenging scenario of imbalance and overlapping. Pattern Analysis and Applications, 2008. 11(3-4): p. 269-280.
44. Xiong, H., J. Wu, and L. Liu. Classification with Class Overlapping: A Systematic Study. in The 2010 International Conference on E-Business Intelligence. 2010.
45. Visa, S., Ralescu, A. Learning Imbalanced And Overlapping Classes Using Fuzzy Sets. in Proceedings of the International Conference of Machine Learning, Workshop on Learning from Imbalanced data Sets (II): Learning with Imbalanced Data Sets II. 2003. Washington.
46. Tomašev, N. and D. Mladenic, Class imbalance and the curse of minority hubs. Knowledge-Based Systems, 2013. 53(0): p. 157-172.
47. Japkowicz, N. Concept-learning in the presence of between-class and within-class imbalances, in Advances in Artificial Intelligence2001, Springer. p. 67-77.
48. Prati, R.C., G.E. Batista, and M.C. Monard, Learning with class skews and small disjuncts, in Advances in Artificial Intelligence-SBIA 20042004, Springer. p. 296-306.
49. Weiss, G.M. The impact of small disjuncts on classifier learning. in Data Mining. 2010. Springer.
50. Jo, T. and N. Japkowicz, Class imbalances versus small disjuncts. SIGKDD Explor. Newsl., 2004. 6(1): p. 40-49.
51. Alejo, R., et al. A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios. Pattern Recognition Letters, 2013. 34(4): p. 380-388.
52. Fatourechi, M., et al. Comparison of evaluation metrics in classification applications with imbalanced datasets. in Machine Learning and Applications, 2008. ICMLA'08. Seventh International Conference on. 2008. IEEE.
53. Stefanowski, J. and S. Wilk, Selective pre-processing of imbalanced data for improving classification performance, in Data Warehousing and Knowledge Discovery2008, Springer. p. 283-292.
54. Nunes, C., et al. Class Imbalance in the Prediction of Dementia from Neuropsychological Data, in Progress in Artificial Intelligence, L. Correia, L. Reis, and J. Cascalho, Editors. 2013, Springer Berlin Heidelberg. p. 138-151.
55. Phung, S.L., A. Bouzerdoum, and G.H. Nguyen, Learning pattern classification tasks with imbalanced data sets. 2009.
56. Chawla, N.V., et al. SMOTE: synthetic minority over-sampling technique. arXiv preprint arXiv:1106.1813, 2002.
57. Guo, H. and H.L. Viktor, Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. ACM SIGKDD Explorations Newsletter, 2004. 6(1): p. 30-39.
58. Yu, T., et al. A hierarchical VQSVM for imbalanced data sets. in Neural Networks, 2007. IJCNN 2007. International Joint Conference on. 2007. IEEE.
59. Yen, S.-J. and Y.-S. Lee, Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications, 2009. 36(3): p. 5718-5727.
60. García, S. and F. Herrera, Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy. Evolutionary Computation, 2009. 17(3): p. 275-306.
61. Provost, F. Machine learning from imbalanced data sets 101. in Proceedings of the AAAI'2000 workshop on imbalanced data sets. 2000.
62. Maloof, M.A. Learning when data sets are imbalanced and when costs are unequal and unknown. in ICML-2003 workshop on learning from imbalanced data sets II. 2003.
63. Mladenic, D. and M. Grobelnik. Feature selection for unbalanced class distribution and naive bayes. in ICML. 1999.
64. Zheng, Z., X. Wu, and R. Srihari, Feature selection for text categorization on imbalanced data. ACM SIGKDD Explorations Newsletter, 2004. 6(1): p. 80-89.
65. Chen, X.-w. and M. Wasikowski. 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. 2008. ACM.
66. Wasikowski, M. and X.-w. Chen, Combating the small sample class imbalance problem using feature selection. Knowledge and Data Engineering, IEEE Transactions on, 2010. 22(10): p. 1388-1400.
67. Hall, M.A. and L.A. Smith. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. in FLAIRS Conference. 1999.
68. Yu, L. and H. Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. in ICML. 2003.
69. Cuaya, G., A. Muñoz-Meléndez, and E.F. Morales, A minority class feature selection method, in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications2011, Springer. p. 417-424.
70. Cuaya, G., A. Muñoz-Meléndez, and E.F. Morales. A Minority Class Feature Selection Method. in Proceedings of the 16th Iberoamerican Congress Conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 2011. Springer-Verlag.
71. Alibeigi, M., S. Hashemi, and A. Hamzeh, Unsupervised Feature Selection Based on the Distribution of Features Attributed to Imbalanced Data Sets. International Journal of Artificial Intelligence and Expert Systems, 2011. 2(1): p. 133-144.
72. Jamali, I., M. Bazmara, and S. Jafari, Feature Selection in Imbalance data sets. International Journal of Computer Science Issues, 2012. 9(3): p. 42-45.
73. Kamal, A.H., et al. Feature Selection for Datasets with Imbalanced Class Distributions. International Journal of Software Engineering and Knowledge Engineering, 2010. 20(02): p. 113-137.
74. Longadge, R. and S. Dongre, Class Imbalance Problem in Data Mining Review. arXiv preprint arXiv:1305.1707, 2013.
75. Khoshgoftaar, T.M., K. Gao, and N. Seliya. Attribute selection and imbalanced data: Problems in software defect prediction. in Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on. 2010. IEEE.
76. Das, S. Filters, wrappers and a boosting-based hybrid for feature selection. in ICML. 2001. Citeseer.
77. Kamal, A.H., et al. Feature selection with biased sample distributions. in Information Reuse & Integration, 2009. IRI'09. IEEE International Conference on. 2009. IEEE.
78. Imam, T., K. Ting, and J. Kamruzzaman, z-SVM: An SVM for Improved Classification of Imbalanced Data, in AI 2006: Advances in Artificial Intelligence, A. Sattar and B.-h. Kang, Editors. 2006, Springer Berlin Heidelberg. p. 264-273.
79. Tang, Y., et al. SVMs modeling for highly imbalanced classification. Trans. Sys. Man Cyber. Part B, 2009. 39(1): p. 281-288.
80. Li, Y. and X. Zhang, Improving k nearest neighbor with exemplar generalization for imbalanced classification, in Advances in knowledge discovery and data mining2011, Springer. p. 321-332.
81. Kriminger, E., J.C. Principe, and C. Lakshminarayan. Nearest Neighbor Distributions for imbalanced classification. in Neural Networks (IJCNN), The 2012 International Joint Conference on. 2012.
82. Fernández, A., M.J. del Jesus, and F. Herrera, Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets. International Journal of Approximate Reasoning, 2009. 50(3): p. 561-577.
83. Japkowicz, N., C. Myers, and M. Gluck. A novelty detection approach to classification. in IJCAI. 1995.
84. Cao, P., D. Zhao, and O. Zaiane, An Optimized Cost-Sensitive SVM for Imbalanced Data Learning, in Advances in Knowledge Discovery and Data Mining2013, Springer. p. 280-292.
85. Cao, P., D. Zhao, and O. Zaiane, A PSO-based Cost-Sensitive Neural Network for Imbalanced Data Classification. 2011.
86. Wang, X., et al. Using SVM with Adaptively Asymmetric Misclassification Costs for Mine-Like Objects Detection. in Machine Learning and Applications (ICMLA), 2012 11th International Conference on. 2012. IEEE.
87. Weiss, G.M., K. McCarthy, and B. Zabar. Cost-sensitive learning vs. sampling: Which is best for handling unbalanced classes with unequal error costs? in DMIN. 2007.
88. Lin, H.-T. Cost-sensitive classification: Status and beyond. in Workshop on Machine Learning Research in Taiwan: Challenges and Directions. 2010.
89. Visa, S., Ralescu, A. Data-Driven Fuzzy Sets For Classification. International Journal of Advanced Intelligence Paradigms 2008 - Vol. 1, No.1 pp. 3-30, 2008.
90. López, V., et al. Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics. Expert Systems with Applications, 2012. 39(7): p. 6585-6608.
91. Kotsiantis, S. and P. Pintelas, Combining bagging and boosting. International Journal of Computational Intelligence, 2004. 1(4): p. 324-333.
92. Machová, K., et al. A comparison of the bagging and the boosting methods using the decision trees classifiers. Computer Science and Information Systems, 2006. 3(2): p. 57-72.
93. Friedman, J., T. Hastie, and R. Tibshirani, Additive Logistic Regression: A Statistical View of Boosting. Ann. Statist, 2000(28(2)): p. 337-374.
94. Breiman, L. Bagging Predictors. Machine Learning, 1996. 24: p. 123-140.
95. Breiman, L. Random Forests. Machine Learning, 2001. 45: p. 5-32.
96. Chawla, N.V., et al. SMOTEBoost: Improving prediction of the minority class in boosting, in Knowledge Discovery in Databases: PKDD 20032003, Springer. p. 107-119.
97. Seiffert, C., et al. RUSBoost: A hybrid approach to alleviating class imbalance. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 2010. 40(1): p. 185-197.
98. Sun, Y., et al. Cost-sensitive boosting for classification of imbalanced data. Pattern recognition, 2007. 40(12): p. 3358-3378.
99. Leskovec, J. and J. Shawe-Taylor. Linear programming boosting for uneven datasets. in ICML. 2003.
100. Wang, S. and X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. in Computational Intelligence and Data Mining, 2009. CIDM'09. IEEE Symposium on. 2009. IEEE.
101. Liu, Y., et al. A study in machine learning from imbalanced data for sentence boundary detection in speech. Computer Speech & Language, 2006. 20(4): p. 468-494.
102. Brown, G. Diversity in neural network ensembles, in Phd Thesis2004, University of Birmingham.
103. Ueda, N. and R. Nakano. Generalization error of ensemble estimators. in Neural Networks, 1996., IEEE International Conference on. 1996. IEEE.
104. Krogh, A. and J. Vedelsby, Neural network ensembles, cross validation, and active learning. Advances in neural information processing systems, 1995: p. 231-238.
105. Brown, G., et al., Diversity creation methods: a survey and categorisation. Information Fusion, 2005. 6(1): p. 5-20.
106. Akbani, R., S. Kwek, and N. Japkowicz, Applying support vector machines to imbalanced datasets, in Machine Learning: ECML 20042004, Springer. p. 39-50.
107. Zhou, Z.-H. and X.-Y. Liu, Training cost-sensitive neural networks with methods addressing the class imbalance problem. Knowledge and Data Engineering, IEEE Transactions on, 2006. 18(1): p. 63-77.
108. Lee, T.-F., M.-Y. Cho, and F.-M. Fang, Features selection of SVM and ANN using particle swarm optimization for power transformers incipient fault symptom diagnosis. International Journal of Computational Intelligence Research, 2007. 3(1): p. 60-65.
109. Lee, C.Y., et al. A hybrid algorithm applied to classify unbalanced data. in Networked Computing and Advanced Information Management (NCM), 2010 Sixth International Conference on. 2010.
110. Cieslak, D.A., et al. Hellinger distance decision trees are robust and skew-insensitive. Data Mining and Knowledge Discovery, 2012. 24(1): p. 136-158.
111. Yu, H., J. Ni, and J. Zhao, ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data. Neurocomputing, 2013. 101(0): p. 309-318.
112. Malof, J.M., M.A. Mazurowski, and G.D. Tourassi, The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support. Neural Networks, 2012. 25(0): p. 141-145.
113. Martino, M.D., et al. Novel Classifier Scheme for Unbalance Problems. Pattern Recognition Letters, 2013.
114. Fernández, A., et al. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems, 2008. 159(18): p. 2378-2398.
115. Hung, C.-M. and Y.-M. Huang, Conflict-sensitivity contexture learning algorithm for mining interesting patterns using neuro-fuzzy network with decision rules. Expert Systems with Applications, 2008. 34(1): p. 159-172.
116. Le, X., C. Mo-Yuen, and L.S. Taylor, Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm. Power Systems, IEEE Transactions on, 2007. 22(1): p. 164-171.
117. Yang, T.-N. and S.-D. Wang, Robust algorithms for principal component analysis. Pattern Recognition Letters, 1999. 20(9): p. 927-933.
118. Alibeigi, M., S. Hashemi, and A. Hamzeh, DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets. Data & Knowledge Engineering, 2012. 81-82(0): p. 67-103.
119. Luukka, P. Nonlinear fuzzy robust PCA algorithms and similarity classifier in bankruptcy analysis. Expert Systems with Applications, 2010. 37(12): p. 8296-8302.
120. Candès, E.J., et al. Robust principal component analysis? Journal of the ACM (JACM), 2011. 58(3): p. 11.
121. Jolliffe, I. Principal component analysis2005: Wiley Online Library.
122. Yang, P., et al. Ensemble-based wrapper methods for feature selection and class imbalance learning, in Advances in Knowledge Discovery and Data Mining2013, Springer. p. 544-555.
123. Yu, E. and S. Cho, Ensemble based on GA wrapper feature selection. Computers & Industrial Engineering, 2006. 51(1): p. 111-116.
124. Liao, J.-J., et al. An ensemble-based model for two-class imbalanced financial problem. Economic Modelling, 2014. 37(0): p. 175-183.
125. Liu, Y., A. An, and X. Huang, Boosting prediction accuracy on imbalanced datasets with SVM ensembles, in Advances in Knowledge Discovery and Data Mining2006, Springer. p. 107-118.
126. Jeatrakul, P., K.W. Wong, and C.C. Fung, Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm, in Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II2010, Springer-Verlag: Sydney, Australia. p. 152-159.
127. Culotta, A. and J. Sorensen. Dependency tree kernels for relation extraction. in Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. 2004. Association for Computational Linguistics.
128. Drummond, C. and R.C. Holte. Exploiting the cost (in) sensitivity of decision tree splitting criteria. in ICML. 2000.
129. Nauck, D. and R. Kruse, Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine, 1999. 16(2): p. 149-169.
130. Angelov, P., E. Lughofer, and X. Zhou, Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst., 2008. 159(23): p. 3160-3182.
131. Kapetanovic, I.M., S. Rosenfeld, and G. Izmirlian, Overview of Commonly Used Bioinformatics Methods and Their Applications. Annals of the New York Academy of Sciences, 2004. 1020(1): p. 10-21.
132. Burges, C.J. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998. 2(2): p. 121-167.
133. Suykens, J.A., et al. Least squares support vector machines. Vol. 4. 2002: World Scientific.
134. Wang, L.X. and J.M. Mendel Generating fuzzy rules by learning from examples. Systems, Man and Cybernetics, IEEE Transactions on, 1992. 22(6): p. 1414-1427.
135. Vapnik, V.N. Statistical Learning Theory1998, New York: John Wiley and Sons.
136. Lewis, D. and W. Gale. A sequential algorithm for training text classifiers. in Proc. 17th Annual Intl. ACM SIGIR Conf. on R&D in Information Retrieval. 1994.
137. Fernández, A., et al. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-Based Systems, 2013.
138. Xue, B., M. Zhang, and W.N. Browne. Multi-objective particle swarm optimisation (PSO) for feature selection. in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. 2012. ACM.
139. Orriols-Puig, A. and E. Bernadó-Mansilla, Evolutionary rule-based systems for imbalanced data sets. Soft Computing, 2009. 13(3): p. 213-225.
140. García, V., et al. Combined effects of class imbalance and class overlap on instancebased classification, in Intelligent Data Engineering and Automated Learning-IDEAL 20062006, Springer. p. 371-378.
141. Martín-Félez, R. and R.A. Mollineda, On the suitability of combining feature selection and resampling to manage data complexity, in Current Topics in Artificial Intelligence2010, Springer. p. 141-150.
142. Hall, M.A. Correlation-based feature selection for machine learning, 1999, The University of Waikato.
143. Lin, W.-J. and J.J. Chen, Class-imbalanced classifiers for high-dimensional data. Briefings in bioinformatics, 2013. 14(1): p. 13-26.
144. Zhang, H., et al. Feature selection for optimizing traffic classification. Computer Communications, 2012. 35(12): p. 1457-1471.
145. Lusa, L. Class prediction for high-dimensional class-imbalanced data. BMC Bioinformatics, 2010. 11(1): p. 523.
146. Liu, B. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions2015: Cambridge University Press.
147. Liu, B. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 2012. 5(1): p. 1-167.
148. Hassan, A., A. Abbasi, and D. Zeng. Twitter Sentiment Analysis: A Bootstrap Ensemble Framework. in Social Computing (SocialCom), 2013 International Conference on. 2013.
149. Guerra, P.C., W. Meira Jr, and C. Cardie. Sentiment analysis on evolving social streams: How self-report imbalances can help. in Proceedings of the 7th ACM international conference on Web search and data mining. 2014. ACM.
150. Li, S., et al. Active learning for imbalanced sentiment classification. in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012. Association for Computational Linguistics.
151. Burns, N., et al. Sentiment Analysis of Customer Reviews: Balanced versus Unbalanced Datasets, in Knowledge-Based and Intelligent Information and Engineering Systems, A. König, et al., Editors. 2011, Springer Berlin Heidelberg. p. 161-170.
152. Fan, M., et al. Intrinsic dimension estimation of data by principal component analysis. arXiv preprint arXiv:1002.2050, 2010.
153. Hasan, S., S.M. Shamsuddin, and N. Lopes, Machine Learning Big Data Framework and Analytics for Big Data Problems. Int. J. Advance Soft Compu. Appl, 2014. 6(2).
154. Wienhofen, L., B.M. Mathisen, and D. Roman, Empirical Big Data Research: A Systematic Literature Mapping. arXiv preprint arXiv:1509.03045, 2015.