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Published in:   Vol. 3 Issue 2 Date of Publication:   December 2014

A Novel Class Imbalance Approach using Cluster Disjuncts

Syed Ziaur Rahman,G. Samuel Vara Prasad Raju

Page(s):   69-80 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.003.002.006 Publisher:   Integrated Intelligent Research (IIR)


  1. J. Wu, S. C. Brubaker, M. D. Mullin, and J. M. Rehg,   "Fast asymmetric learning for cascade face detection",   IEEE Trans. Pattern Anal. Mach.Intell.   ,Vol.30   ,Issue 3   ,2008
    View Artical

  2. G. M.Weiss,   "Mining with rarity: A unifying framework",   ACM SIGKDD Explor. Newslett   ,Vol.6   ,Issue 1   ,2004
    View Artical

  3. N. V. Chawla, N. Japkowicz, and A. Kolcz, Eds,   "Special Issue Learning Imbalanced Datasets",   SIGKDD Explor. Newsl.,   ,Vol.6   ,Issue 1   ,2004
    View Artical

  4. W.-Z. Lu and D.Wang,   "Ground-level ozone prediction by support vector machine approach with a cost-sensitive classificatio",   Sci.Total. Enviro   ,Vol.395   ,2008
    View Artical

  5. Y.-M. Huang, C.-M. Hung, and H. C. Jiau,   "Evaluation of neural networks and data mining methods on a credit assessment task for class imbalanc",   Nonlinear Anal. R. World Appl   ,Vol.7   ,Issue 4   ,2006
    View Artical

  6. D. Cieslak, N. Chawla, and A. Striegel,   "Combating imbalance in network intrusion datasets",   in IEEE Int. Conf. Granular Comput   ,2006
    View Artical

  7. M. A. Mazurowski, P. A. Habas, J. M. Zurada, J. Y. Lo, J. A. Baker, and G. D. Tourassi,   "Training neural network classifiers for medical decision making: The effects of imbalanced datasets ",   Neural Netw   ,Vol.21   ,2008
    View Artical

  8. A. Freitas, A. Costa-Pereira, and P. Brazdil,   "Cost-sensitive decision trees applied to medical data",   in Data Warehousing Knowl. Discov.(Lecture Notes S
    View Artical

  9. K.Kilic?,O? zgeUncu and I. B. Tu?rksen,   "Comparison of different strategies of utilizing fuzzy clustering in structure identification",   Inf.Sci   ,Vol.177   ,Issue 23   ,2007
    View Artical

  10. M. E. Celebi, H. A. Kingravi, B. Uddin, H. Iyatomi, Y. A. Aslandogan,W. V. Stoecker, and R. H. Moss,   "A methodological approach to the classification of dermoscopy images",   Comput.Med. Imag. Grap   ,Vol.31   ,Issue 6   ,2007
    View Artical

  11. X. Peng and I. King,   "Robust BMPM training based on second-order cone programming and its application in medical diagnosis",   Neural Netw.   ,Vol.21   ,2008
    View Artical

  12. Rukshan Batuwita and Vasile Palade,   "Fuzzy Support Vector Machines for Class Imbalance Learning",   IEEE TRANSACTIONS ON FUZZY SYSTEMS   ,Vol.18   ,Issue 3   ,2010
    View Artical

  13. N. Japkowicz and S. Stephen,   "The Class Imbalance Problem: A Systematic Study",   Intelligent Data Analysis   ,Vol.6   ,2002
    View Artical

  14. M. Kubat and S. Matwin,   "Addressing the Curse of Imbalanced Training Sets: One-Sided Selection",   Proc. 14th Int?l Conf. Machine Learning,   ,1997
    View Artical

  15. G.E.A.P.A. Batista, R.C. Prati, and M.C. Monard,   "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data",   SIGKDD Explorations   ,Vol.6   ,2004
    View Artical

  16. Siti Khadijah Mohamada, Zaidatun Tasir,   "Educational data mining: A review",   Procedia - Social and Behavioral Sciences   ,2013
    View Artical

  17. Hongzhou Sha, Tingwen Liu, Peng Qin, Yong Sun, Qingyun Liu,   "EPLogCleaner: Improving Data Quality of Enterprise Proxy Logs for Efficient Web Usage Mining",   Procedia Computer Science   ,2013
    View Artical

  18. M.S.B. PhridviRaj, C.V. GuruRao,   "Data mining ? past, present and future ? a typical survey on data Streams",   Procedia Technology   ,2014
    View Artical

  19. Chumphol Bunkhumpornpat, Krung Sinapiromsaran, Chidchanok Lursinsap,   "DBSMOTE: Density-Based Synthetic Minority Oversampling Technique",   Appl Intell   ,2012
    View Artical

  20. Mat?as Di Martino, Alicia Fern?ndez, Pablo Iturralde, Federico Lecumberry,   "Novel classifier scheme for imbalanced problems",   Pattern Recognition Letters   ,2013
    View Artical

  21. V. Garcia, J.S. Sanchez , R.A. Mollineda,   "On the effectiveness of preprocessing methods when dealing with different levels of class imbalance",   Knowledge-Based Systems   ,2012
    View Artical

  22. Mar?a Dolores P?rez-Godoy, Alberto Fern?ndez, Antonio Jes?s Rivera,Mar?a Jos? del Jesus,   "Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets",   Pattern Recognition Letters   ,2010
    View Artical

  23. Der-Chiang Li, Chiao-WenLiu, SusanC.Hu,   "A learning method for the class imbalance problem with medical data sets",   Computers in Biology and Medicine   ,2010
    View Artical

  24. Enhong Che, Yanggang Lin, Hui Xiong, Qiming Luo, Haiping Ma,   "Exploiting probabilistic topic models to improve text categorization under class imbalance",   Information Processing and Management   ,2011
    View Artical

  25. Alberto Fern?ndez, Mar?a Jos? del Jesus, Francisco Herrera,   "On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imba",   Information Sciences   ,2010
    View Artical

  26. Z. Chi, H. Yan, T. Pham,   "Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition",   World Scientific   ,1996
    View Artical

  27. H. Ishibuchi, T. Yamamoto, T. Nakashima,   "Hybridization of fuzzy GBML approaches for pattern classification problems",   IEEE Transactions on System, Man and Cybernetics B   ,2005
    View Artical

  28. J. Burez, D. Van den Poel,   "Handling class imbalance in customer churn prediction",   Expert Systems with Applications   ,2009
    View Artical

  29. Che-Chang Hsu, Kuo-Shong Wang, Shih-Hsing Chang,   "Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization",   Expert Systems with Applications   ,2011
    View Artical

  30. Alberto Fern?ndez, Mar?a Jos? del Jesus, Francisco Herrera,   "On the influence of an adaptive inference system in fuzzy rule based classification systems for imba",   Expert Systems with Applications   ,2009
    View Artical

  31. Jordan M. Malof, Maciej A. Mazurowski, Georgia D. Tourassi,   "The effect of class imbalance on case selection for case-based classifiers: An empirical study in th",   Neural Networks   ,2012
    View Artical

  32. J.R. Quinlan,,   "Induction of Decision Trees",   Machine Learning   ,Vol.1   ,Issue 1   ,1986
    View Artical

  33. T. Jo and N. Japkowicz,   "Class Imbalances versus Small Disjuncts",   ACM SIGKDD Explorations Newsletter   ,Vol.6   ,Issue 1   ,2004
    View Artical

  34. N. Japkowicz,   "Class Imbalances: Are We Focusing on the Right Issue?",   Proc. Int?l Conf. Machine Learning, Workshop Learn   ,2003
    View Artical

  35. R.C. Prati, G.E.A.P.A. Batista, and M.C. Monard,   "Class Imbalances versus Class Overlapping: An Analysis of a Learning
    View Artical

  36. G.M. Weiss,   "Mining with Rarity: A Unifying Framework",   ACM SIGKDD Explorations Newsletter   ,Vol.6   ,Issue 1   ,2004
    View Artical

  37. N. Chawla, K. Bowyer, and P. Kegelmeyer,   "SMOTE: Synthetic minority over-sampling technique",   J. Artif. Intell. Res   ,Vol.16   ,2002
    View Artical

  38. Witten, I.H. and Frank, E.,   "Data Mining: Practical machine learning tools and techniques",   2nd edition Morgan Kaufmann, San Francisco   ,2005
    View Artical

  39. Mohamed Bekkar and Dr. Taklit Akrouf Alitouche,   "Imbalanced Data Learning Approaches Review",   International Journal of Data Mining & Knowledge M   ,Vol.3   ,Issue 4   ,2013
    View Artical

  40. J. R. Quinlan,,   "C4.5: Programs for Machine Learning",   1st ed. San Mateo, CA: Morgan Kaufmann Publishers   ,1993
    View Artical