A cost-sensitive multi-criteria quadratic programming model for imbalanced dataFootnote

Please note this paper has been re-typeset by Taylor & Francis from the manuscript originally provided to the previous publisher.

References

• Atkeson, C., Moore, A., & Schaal, S. (1996). Locally weighted learning. Artificial Intelligence Review, 11(1), 11–73.
   Web of Science ®Google Scholar
• Barros, R. C., Basgalupp, M. P., & de Carvalho, A. C. P. L. F. (2012). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Re, 42(2), 291–312.
   Web of Science ®Google Scholar
• Beyan, C., & Fisher, R. (2015). Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition, 48(5), 1653–1672.
   Web of Science ®Google Scholar
• Bradely, P. S., Fayyad, U. M., & Mangasarian, O. L. (1999). Mathematical programming for data mining: formulations and challenges. Informs Journal On Computing, 11(3), 217–238.
   Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
   Web of Science ®Google Scholar
• Campadelli, P., Casiraghi, E., & Valentini, G. (2005). Support vector machines for candidate nodules classification. Neurocomputing, 68, 281–288.
   Web of Science ®Google Scholar
• Cao P, Zhao D. Z., & Zaiane O. (2013) Measure Oriented Cost- Sensitive SVM for 3D nodule detection, 35th Annual International Conference of the IEEE EMBS Osaka, Japan, pp 3–7.
   Google Scholar
• Chang, C. T. (2013). On product classification with various membership functions and binary behavior. Journal of the Operational Research Society, 65(1), 141–150.
   Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2011). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1), 321–357.
   Google Scholar
• Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Editorial: special issue on learning from imbalanced data sets (pp. 1–6). Banff: SIGKDD Explorations, Learning (ICML).
   Google Scholar
• Christopher, J., & Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167.
   Web of Science ®Google Scholar
• Christianini, N., & Shawe-tayor, J. (2000). An introduction to support vector machines and other kenel-based learning methods. Cambridge: Cambridge University Press.
   Google Scholar
• Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1), 10–18.
   Google Scholar
• Hart P. E. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory.
   Google Scholar
• He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
   Web of Science ®Google Scholar
• Hwang, K., Lee, K., Lee, C., & Park, S. (2014). Multi-class classification using a signomial function. Journal of the Operational Research Society, 66(3), 434–449.
   Web of Science ®Google Scholar
• Fernańdez, A., García, S., Jesus, M. J. D., & Herrera, F. (2008). A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems, 159(18), 2378–2398.
   Web of Science ®Google Scholar
• Ferna′ndez, A., Jesus, M. J. D., & Herrera, F. (2009). Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets. International Journal of Approximate Reasoning, 50(3), 561–577.
   Web of Science ®Google Scholar
• Ferri, C., Hernańdez-Orallo, J., & Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters, 30(1), 27–38.
   Web of Science ®Google Scholar
• Freed, N., & Glover, F. (1981). Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research, 7(1), 44–60.
   Web of Science ®Google Scholar
• Freund Y., & Schapire R. E. (1996). Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, pp. 148–156.
   Google Scholar
• Fung G. (2003). Machine learning and data mining via mathematical programming-based support vector machines (PhD thesis).The University of Wisconsin-Madison.
   Google Scholar
• Garcia-Palomares, U. M., & Manzanilla-Salazar, O. (2012). Novel linear programming approach for building a piecewise nonlinear binary classifier with a priori accuracy. Decision Support Systems, 52(3), 717–728.
   Web of Science ®Google Scholar
• Kou, G., Peng, Y., Chen, Z., & Shi, Y. (2005). Discovering credit cardholders’ behavior by multiple criteria linear programming. Annals of Operations Research, 135(1), 261–274.
   Web of Science ®Google Scholar
• Kou, G., Peng, Y., Chen, Z., & Shi, Y. (2009). Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection. Information Sciences, 179(4), 371–381.
   Web of Science ®Google Scholar
• Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology and Decision Making, 11(1), 197–225.
   Web of Science ®Google Scholar
• Kubat M and Matwin S (1997). Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th international conference on machine learning (ICML’97), pp 179–186.
   Google Scholar
• Li, A. H., Shi, Y., & He, J. (2008). MCLP-based methods for improving ‘Bad’ catching rate in credit cardholder behavior analysis. Applied Soft Computing, 8, 1259–1265.
   Web of Science ®Google Scholar
• Lichman M. (2013) UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.
   Google Scholar
• Liu, X. Y., & Zhou, Z. H. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18(1), 66–67.
   Web of Science ®Google Scholar
• Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM Computing Surveys, 45(2), 1–35.
   Web of Science ®Google Scholar
• López V., Fernández A., García S. X., Palade V., & Herrera F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250(1), 113–141.
   Web of Science ®Google Scholar
• Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(11), 2579–2605.
   Google Scholar
• Martens, D., & Provost, F. (2014). Explaining data-driven document classifications. MIS Quarterly, 38(1), 73–99.
   Web of Science ®Google Scholar
• Masnadi-Shirazi N., Vasconcelos A., & Iranmehr A. (2015) Cost- sensitive Support Vector Machines. Journal of Machine Learning Research. arXiv:1212.0975V2.
   Google Scholar
• Min, F., He, H. P., Qian, Y. H., & Zhu, W. (2011). Test-cost-sensitive attribute reduction. Information Sciences, 181(22), 4928–4942.
   Web of Science ®Google Scholar
• Pavlidis, N. G., Tasoulis, D. K., Adams, N. M., & Hand, D. J. (2012). Adaptive consumer credit classification. Journal of the Operational Research Society, 63(12), 1645–1654.
   Web of Science ®Google Scholar
• Peng, Y., Kou, G., Shi, Y., & Chen, Z. (2008a). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology and Decision Making, 7(4), 639–682.
   Web of Science ®Google Scholar
• Peng, Y., Kou, G., Shi, Y., & Chen, Z. (2008b). A Multi-criteria convex quadratic programming model for credit data analysis. Decision Support Systems, 44(4), 1016–1030.
   Web of Science ®Google Scholar
• Scholkopf, B., & Smola, A. J. (2002). Learning with kernels. Cambridge: MIT Press.
   Google Scholar
• Seiffert C., Khoshgoftaar T. H., Van Hulse J. and Napolitano A. (2010) RUSBoost: a hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 40(1):185–209.
   Web of Science ®Google Scholar
• Shi, Y. (2010). Multiple criteria optimization-based data mining methods and applications: a systematic survey. Knowledge and Information Systems, 24(3), 369–391.
   Web of Science ®Google Scholar
• Shi, Y. H., Gao, Y., Wang, R. L., Zhang, Y., & Wang, D. (2013). Transductive cost-sensitive lung cancer image classification. Applied Intelligence, 38(1), 16–28.
   Web of Science ®Google Scholar
• Soda, P. (2011). A multi-objective optimisation approach for class imbalance learning. Pattern Recognition, 44(8), 1801–1810.
   Web of Science ®Google Scholar
• Sun, A., Lim, E. P., & Liu, Y. (2009). On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems, 48(1), 191–201.
   Web of Science ®Google Scholar
• Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007). Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition, 40(12), 3358–3378.
   Web of Science ®Google Scholar
• Thai-Nghe, N., Gan TPer Z., & Schmidt Thieme L. (2010). Cost- sensitive learning methods for imbalanced data. Proceeding of IEEE IJCNN10 (pp. 1–8). Barcelona: IEEE CS.
   Google Scholar
• Ting, K. M. (2002). An instance weighting method to induce cost- sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering, 14(3), 659–665.
   Web of Science ®Google Scholar
• Tomek, I. (1976). Two modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics, 6(11), 769–772.
   Google Scholar
• Tsai, C., Chang, L., & Chiang, H. (2009). Forecasting of ozone episode days by cost-sensitive neural network methods. Science of the Total Environment, 407(6), 2124–2135.
   PubMed Web of Science ®Google Scholar
• Vapnik V. N. (1982) Estimation of dependences based on empirical data [in Russian], Nauka, Moscow, 1979 (English translation). New York: Springer Verlag.
   Google Scholar
• Vapnik, V. N. (1995). The nature of statistical learning theory. New York, NY: Springer.
   Google Scholar
• Vapnik, V. N. (2000). The nature of statistical learning theory (2nd ed.). New York: Springer.
   Google Scholar
• Vapnik, V. N., & Chapelle, O. (2000). Bounds on error expectation for support vector machines. Neural Computation., 12(9), 2013–2036.
   PubMed Web of Science ®Google Scholar
• Wang, G., Sun, J., & Ma, J. (2014a). Sentiment classification: the contribution of ensemble learning. Decision Support Systems, 57, 77–93.
   Web of Science ®Google Scholar
• Wang, J., Zhao, P., & Steven, C. H. H. (2014b). Cost-sensitive online classification. IEEE Transactions on Knowledge and Data Engineering, 26(10), 2425–2438.
   Web of Science ®Google Scholar
• Weiss G. M. (2010) The impact of small disjuncts on classifier learning. In Stahlbock R, Crone S. F., Lessmann S (eds.) Data mining: annals of information systems. Springer:Berlin, vol. 8, pp. 193–226.
   Google Scholar
• Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.
   Google Scholar
• Wilson, D. (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics, 2(3), 408–421.
   Web of Science ®Google Scholar
• Wolfe, P. (1961). A duality theorem for nonlinear programming. Quarterly Journal of Applied Mathematics, 19(3), 239–244.
   Google Scholar
• Yang, Q., & Wu, X. (2006). 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making, 5(4), 597–604.
   Web of Science ®Google Scholar
• Yue, W. T., & Cakanyildiri, M. (2010). A cost-based analysis of intrusion detection system configuration under active or passive response. Decision Support Systems, 50(1), 21–31.
   Web of Science ®Google Scholar
• Zhao, H. M., Sinha, A. P., & Bansal, G. (2011). An extended tuning method for cost-sensitive regression and forecasting. Decision Support Systems, 51(3), 372–383.
   Web of Science ®Google Scholar
• Zhang, J. L., Shi, Y., & Zhang, P. (2009). Several multi-criteria programming methods for classification. Computers & Operations Research, 36(3), 823–836.
   Web of Science ®Google Scholar