Diversity analysis on imbalanced data sets by using ensemble models
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[1] Nitesh V. Chawla,et al. Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets , 2007, MCS.
[2] G. Yule,et al. On the association of attributes in statistics, with examples from the material of the childhood society, &c , 1900, Proceedings of the Royal Society of London.
[3] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[4] Nitesh V. Chawla,et al. Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.
[5] Ludmila I. Kuncheva,et al. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.
[6] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[7] JapkowiczNathalie,et al. The class imbalance problem: A systematic study , 2002 .
[8] Cen Li,et al. Classifying imbalanced data using a bagging ensemble variation (BEV) , 2007, ACM-SE 45.
[9] Ralescu Anca,et al. ISSUES IN MINING IMBALANCED DATA SETS - A REVIEW PAPER , 2005 .
[10] Herna L. Viktor,et al. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.
[11] Gustavo E. A. P. A. Batista,et al. A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.
[12] José Martínez Sotoca,et al. Combined Effects of Class Imbalance and Class Overlap on Instance-Based Classification , 2006, IDEAL.
[13] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[14] R. A. Mollineda,et al. The class imbalance problem in pattern classification and learning , 2009 .
[15] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[16] Rosa Maria Valdovinos,et al. Class-dependant resampling for medical applications , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).
[17] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[18] I. Tomek,et al. Two Modifications of CNN , 1976 .
[19] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[20] MonardMaria Carolina,et al. A study of the behavior of several methods for balancing machine learning training data , 2004 .
[21] Peter Tiño,et al. Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..
[22] Yong Zhao,et al. All Zero Block Detection Based on Statistics for AVS-M Intra Frame Prediction , 2008, 2008 International Symposium on Intelligent Information Technology Application Workshops.