Class Imbalance Learning

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  Naonori Ueda,et al.  Generalization error of ensemble estimators , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[3]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[4]  R. Polikar,et al.  Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.

[5]  Kai Ming Ting,et al.  A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.

[6]  Nathalie Japkowicz,et al.  A Novelty Detection Approach to Classification , 1995, IJCAI.

[7]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[8]  Rosa Maria Valdovinos,et al.  Class-dependant resampling for medical applications , 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

[9]  Xingquan Zhu,et al.  Lazy Bagging for Classifying Imbalanced Data , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[10]  Thomas G. Dietterich Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[11]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[12]  Robert C. Holte,et al.  C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .

[13]  Vipin Kumar,et al.  Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[14]  Robert P. W. Duin,et al.  Limits on the majority vote accuracy in classifier fusion , 2003, Pattern Analysis & Applications.

[15]  Thomas G. Dietterich An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[16]  Cen Li,et al.  Classifying imbalanced data using a bagging ensemble variation (BEV) , 2007, ACM-SE 45.

[17]  Kagan Tumer,et al.  Linear and Order Statistics Combiners for Pattern Classification , 1999, ArXiv.

[18]  Francisco Herrera,et al.  A Proposal of Evolutionary Prototype Selection for Class Imbalance Problems , 2006, IDEAL.

[19]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[20]  José Salvador Sánchez,et al.  Strategies for learning in class imbalance problems , 2003, Pattern Recognit..

[21]  Xin Yao,et al.  Ensemble learning via negative correlation , 1999, Neural Networks.

[22]  Gustavo E. A. P. A. Batista,et al.  Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior , 2004, MICAI.

[23]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[24]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[25]  Nitesh V. Chawla,et al.  C4.5 and Imbalanced Data sets: Investigating the eect of sampling method, probabilistic estimate, and decision tree structure , 2003 .

[26]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[27]  Xin Yao,et al.  Simultaneous training of negatively correlated neural networks in an ensemble , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Giorgio Valentini,et al.  Ensembles of Learning Machines , 2002, WIRN.

[29]  Xin Yao,et al.  Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..

[30]  Nikunj C. Oza,et al.  Online Ensemble Learning , 2000, AAAI/IAAI.

[31]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[32]  Sungzoon Cho,et al.  Observational Learning Algorithm for an Ensemble of Neural Networks , 2002, Pattern Analysis & Applications.

[33]  Francisco Herrera,et al.  Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study , 2003, IEEE Trans. Evol. Comput..

[34]  Rosa Maria Valdovinos,et al.  The Imbalanced Training Sample Problem: Under or over Sampling? , 2004, SSPR/SPR.

[35]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[36]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[37]  Xin Yao,et al.  An analysis of diversity measures , 2006, Machine Learning.

[38]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[39]  Anders Krogh,et al.  Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[40]  Taghi M. Khoshgoftaar,et al.  Using evolutionary sampling to mine imbalanced data , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).

[41]  Dimitris Kanellopoulos,et al.  Handling imbalanced datasets: A review , 2006 .

[42]  Peter Tiño,et al.  Managing Diversity in Regression Ensembles , 2005, J. Mach. Learn. Res..

[43]  Victor S. Sheng,et al.  Cost-Sensitive Learning and the Class Imbalance Problem , 2008 .

[44]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[45]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[46]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[47]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[48]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[49]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[50]  Nitesh V. Chawla,et al.  Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets , 2007, MCS.

[51]  Xin Yao,et al.  Diversity analysis on imbalanced data sets by using ensemble models , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[52]  Yang Wang,et al.  Boosting for Learning Multiple Classes with Imbalanced Class Distribution , 2006, Sixth International Conference on Data Mining (ICDM'06).

[53]  Xin Yao,et al.  Evolving a cooperative population of neural networks by minimizing mutual information , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[54]  Xin Yao,et al.  Ensemble Learning Using Multi-Objective Evolutionary Algorithms , 2006, J. Math. Model. Algorithms.

[55]  Xin Yao,et al.  Diversity exploration and negative correlation learning on imbalanced data sets , 2009, 2009 International Joint Conference on Neural Networks.

[56]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[57]  Gary M. Weiss Mining with rarity: a unifying framework , 2004, SKDD.

[58]  Zhi-Hua Zhou,et al.  ON MULTI‐CLASS COST‐SENSITIVE LEARNING , 2006, Comput. Intell..

[59]  Taghi M. Khoshgoftaar,et al.  Experimental perspectives on learning from imbalanced data , 2007, ICML '07.

[60]  Ralescu Anca,et al.  ISSUES IN MINING IMBALANCED DATA SETS - A REVIEW PAPER , 2005 .