References
[1]. Juanli Hu, Jiabin Deng & Mingxiang Sui (2009). A New Approach for Decision Tree Based on Principal Component Analysis, Proceedings of Conference on Computational Intelligence and Software Engineering(pp 1-4).
[2]. Huimin Zhao & Atish P. Sinha (2005, September). An Efficient Algorithm for Generating Generalized Decision Forests, IEEE Transactions on Systems, Man, and Cybernetics -Part A : Systems and Humans(VOL.35,NO.5, pp: 287-299).
[3]. D. Liu, C. Lai & W. Lee (2009). A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation, Information Sciences 179 (20), pp: 3505-3519.
[4]. M. Mitchell (1997). Machine Learning. McGraw Hill, New York.
[5]. David Hand, HeikkiMannila, & Padhraic Smyth (2001, August). Principles of Data Mining. MIT Press.
[6]. Jiawei Han & MichelineKamber (2000,April). Data Mining: Concepts and Techniques. Morgan Kaufmann,
[7]. J. Quinlan (1993). C4.5 Programs for Machine Learning, San Mateo, CA:Morgan Kaufmann.
[8]. L. Breiman, J. Friedman, R. Olshen & C. Stone (1984). Classification and Regression Trees. Belmont,CA: Wadsworth.
[9]. J. Quinlan (1986). Induction of decision trees, Machine Learning – 1: 81-106.
[10]. J. Wu, S. C. Brubaker, M. D. Mullin, & J. M. Rehg (2008,Mar). “Fast asymmetric learning for cascade face detection,” IEEE Trans. Pattern Anal. Mach. Intell.,(vol. 30, no. 3, pp. 369–382).
[11]. N. V. Chawla, N. Japkowicz, & A. Kotcz, Eds (2003). Proc. ICML Workshop Learn. Imbalanced Data Sets.
[12]. N. Japkowicz, Ed.(2000). Proc. AAAI Workshop Learn. Imbalanced Data Sets.
[13]. G. M.Weiss (2004, June). “Mining with rarity: A unifying framework,” ACM SIGKDD Explor. Newslett., (vol. 6, no. 1, pp. 7–19).
[14]. N. V. Chawla, N. Japkowicz, and A. Kolcz, Eds. (2004). Special Issue Learning Imbalanced Datasets, SIGKDD Explor. Newsl.,vol. 6(1).
[15]. W.-Z. Lu & D.Wang (2008). “Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme,” Sci. Total. Enviro., (vol. 395, no.2-3, pp. 109–116).
[16]. Y.-M. Huang, C.-M. Hung, & H. C. Jiau, (2006). “Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem,” Nonlinear Anal. R. World Appl., (vol. 7, no. 4, pp. 720–747).
[17]. D. Cieslak, N. Chawla, and A. Striegel (2006). “Combating imbalance in network intrusion datasets,” in IEEE Int. Conf. Granular Comput., , (pp. 732–737).
[18]. M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, & G. D. Tourassi (2008). “Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance,” Neural Netw.,(vol. 21, no. 2–3, pp. 427–436).
[19]. A. Freitas, A. Costa-Pereira, and P. Brazdil. “Cost-sensitive decision trees applied to medical data (2007),” in Data Warehousing Knowl. Discov. (Lecture Notes Series in Computer Science), I. Song, J. Eder, and T. Nguyen, Eds., Berlin/Heidelberg, Germany: Springer, (vol. 4654, pp. 303–312).
[20]. K.Kilic¸,O¨ zgeUncu & I. B. Tu¨rksen, (2007). “Comparison of different strategies of utilizing fuzzy clustering in structure identification,” Inf. Sci., (vol. 177, no. 23, pp. 5153–5162).
[21]. M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan, W. V. Stoecker, & R. H. Moss (2007), “A methodological approach to the classification of dermoscopy images,” Comput.Med. Imag. Grap.,(vol. 31, no. 6, pp. 362–373).
[22]. X. Peng & I. King (2008), “Robust BMPM training based on second-order cone programming and its application in medical diagnosis,” Neural Netw., (vol. 21, no. 2–3, pp. 450–457).
[23]. RukshanBatuwita & Vasile Palade (2010,June) FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning, IEEE TRANSACTIONS ON FUZZY SYSTEMS, (VOL. 18, NO. 3,pp no:558-571).
[24]. N. Japkowicz and S. Stephen (2002), “The Class Imbalance Problem: A Systematic Study,” Intelligent Data Analysis (vol. 6, pp. 429-450).
[25]. M. Kubat and S. Matwin (1997), “Addressing the Curse of Imbalanced Training Sets: One-Sided Selection,” Proc. 14th Int'l Conf. Machine Learning,(pp. 179-186).
[26]. G.E.A.P.A. Batista, R.C. Prati, & M.C. Monard (2004), “A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data,” SIGKDD Explorations,6(1): 20-29.
[27]. D. Cieslak & N. Chawla (2008), “Learning decision trees for unbalanced data,” in Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer-Verlag,( pp. 241–256).
[28]. G.Weiss(2004), “Mining with rarity: A unifying framework,” SIGKDD Explor. Newslett., (vol. 6, no. 1, pp. 7–19).
[29]. N. Chawla, K. Bowyer, & P. Kegelmeyer (2002), “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.,( vol. 16, pp. 321–357).
[30]. J. Zhang & I. Mani (2003), “KNN approach to unbalanced data distributions: A case study involving information extraction,” in Proc. Int. Conf. Mach. Learning, Workshop: Learning Imbalanced Data Sets, Washington, DC,(pp. 42–48).
[31]. A. Asuncion D. Newman. (2007). UCI Repository of Machine Learning Database (School of Information and Computer Science), Irvine, CA: Univ. of California [Online]. Available: http://www.ics.uci.edu/∼mlearn/MLRepository. html.
[32]. T. Jo & N. Japkowicz(2004). “Class imbalances versus small disjuncts,” ACM SIGKDD Explor. Newslett.,( vol. 6, no. 1, pp. 40–49).
[33]. S. Zou, Y. Huang, Y. Wang, J. Wang, & C. Zhou (2008). “SVM learning from imbalanced data by GA sampling for protein domain prediction,” in Proc. 9th Int. Conf. Young Comput. Sci., Hunan, China, , (pp. 982– 987).
[34]. Jinguha Wang, JaneYou ,QinLi, & YongXu (2012). ”Extract minimum positive and maximum negative features for imbalanced binary classification”, Pattern Recognition 45 : 1136–1145.
[35]. Iain Brown, Christophe Mues (2012). “An experimental comparison of classification algorithms for imbalanced credit scoring data sets”, Expert Systems with Applications 39 : 3446–3453.
[36]. Salvador Garc?´a, Joaqu?´nDerrac, Isaac Triguero, Cristobal J. Carmona, Francisco Herrera (2012). “Evolutionary-based selection of generalized instances for imbalanced classification”, Knowledge-Based Systems 25 : 3–12.
[37]. Jin Xiao, Ling Xie, Changzheng He & Xiaoyi Jiang (2012). ” Dynamic classifier ensemble model for customer classification with imbalanced class distribution”, Expert Systems with Applications 39 : 3668–3675.
[38]. Victoria López, Alberto Fernández, Jose G. Moreno-Torres, Francisco Herrera (2012). “Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics”, Expert Systems with Applications 39 : 6585–6608.
[39]. Yang Yong. “The Research of Imbalanced Data Set of Sample Sampling Method Based on K-Means Cluster and Genetic Algorithm”, Energy Procedia 17 : 164 – 170.
[40]. Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, & Amri Napolitano (2010,Jan). ”RUSBoost: A Hybrid Approach to Alleviating Class Imbalance”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART A: SYSTEMS AND HUMANS,(VOL. 40, NO. 1 pp 185-197).
[41]. V. Garcia, J.S. Sanchez , & R.A. Mollineda (2012). ”On the effectiveness of preprocessing methods when dealing with different levels of class imbalance”, Knowledge-Based Systems 25 : 13–21.
[42]. María Dolores Pérez-Godoy, Alberto Fernández, Antonio Jesús Rivera, María José del Jesus (2010). ”Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets”, Pattern Recognition Letters 31 :2375–2388.
[43]. Der-Chiang Li, Chiao-WenLiu, & SusanC.Hu (2010). ” A learning method for the class imbalance problem with medical data sets”, Computers in Biology and Medicine 40 : 509–518.
[44]. EnhongChe, Yanggang Lin, HuiXiong, QimingLuo, & Haiping Ma (2011). “Exploiting probabilistic topic models to improve text categorization under class imbalance”, Information Processing and Management 47 : 202–214.
[45]. Alberto Fernández, María Josédel Jesus, & Francisco Herrera (2010). ”On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets”, Information Sciences 180 : 1268–1291.
[46]. Z. Chi, H. Yan, T. Pham, (1996). Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition, World Scientific.
[47]. H. Ishibuchi, T. Yamamoto, T. Nakashima (2005). “Hybridization of fuzzy GBML approaches for pattern classification problems”, IEEE Transactions on System, Man and Cybernetics B 35 (2) : 359–365.
[48]. J. Burez, D. Van den Poel (2009). ”Handling class imbalance in customer churn prediction”, Expert Systems with Applications 36 : 4626–4636.
[49]. Che-Chang Hsu, Kuo-Shong Wang, Shih-Hsing Chang (2011). ”Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization”, Expert Systems with Applications 38: 4698–4704.
[50]. Alberto Fernández, María José del Jesus & Francisco Herrera(2009). ”On the influence of an adaptive inference system in fuzzy rule based classification systems for imbalanced data-sets”, Expert Systems with Applications 36 : 9805–9812.
[51]. Jordan M. Malof, Maciej A. Mazurowski, Georgia D. Tourassi (2012).” The effect of class imbalance on case selection for case-based classifiers: An empirical study in the context of medical decision support”, Neural Networks 25 : 141–145.
[52]. Blake, C., & Merz, C.J. (2000). UCI repository ofmachinelearning databases. Machine-readable datarepository, Department of Information and Computer Science, University of California at Irvine, Irvine, CA. at http://www.ics.uci.edu/mlearn/MLRepository.html.