Editorial: special issue on learning from imbalanced data sets

[1]  Nitesh V. Chawla,et al.  Classification and knowledge discovery in protein databases , 2004, J. Biomed. Informatics.

[2]  Rohini K. Srihari,et al.  Feature selection for text categorization on imbalanced data , 2004, SKDD.

[3]  Taeho Jo,et al.  Class imbalances versus small disjuncts , 2004, SKDD.

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

[5]  M. Dolores del Castillo,et al.  A multistrategy approach for digital text categorization from imbalanced documents , 2004, SKDD.

[6]  Damminda Alahakoon,et al.  Minority report in fraud detection: classification of skewed data , 2004, SKDD.

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

[8]  Adam Kowalczyk,et al.  Extreme re-balancing for SVMs: a case study , 2004, SKDD.

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

[10]  Philip S. Yu,et al.  Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.

[11]  John Langford,et al.  Cost-sensitive learning by cost-proportionate example weighting , 2003, Third IEEE International Conference on Data Mining.

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

[13]  Johannes Fürnkranz,et al.  An Analysis of Rule Evaluation Metrics , 2003, ICML.

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

[15]  George Forman,et al.  An Extensive Empirical Study of Feature Selection Metrics for Text Classification , 2003, J. Mach. Learn. Res..

[16]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[17]  Peter D. Turney.  Types of Cost in Inductive Concept Learning , 2002, ArXiv.

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

[19]  JapkowiczNathalie,et al.  The class imbalance problem: A systematic study , 2002 .

[20]  Kai Ming Ting,et al.  An Instance-weighting Method to Induce Cost-sensitive Trees , 2001 .

[21]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

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

[23]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.

[24]  Charles Elkan,et al.  Shared challenges in data mining and computational biology (abstract of invited talk) , 2001, BIOKDD.

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

[26]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[27]  David M. J. Tax,et al.  One-class classification , 2001 .

[28]  Nathalie Japkowicz,et al.  Concept-Learning in the Presence of Between-Class and Within-Class Imbalances , 2001, Canadian Conference on AI.

[29]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[30]  Robert C. Holte,et al.  Explicitly representing expected cost: an alternative to ROC representation , 2000, KDD '00.

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

[32]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[33]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[34]  Dunja Mladenic,et al.  Feature Selection for Unbalanced Class Distribution and Naive Bayes , 1999, ICML.

[35]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[36]  Salvatore J. Stolfo,et al.  Toward Scalable Learning with Non-Uniform Class and Cost Distributions: A Case Study in Credit Card Fraud Detection , 1998, KDD.

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[39]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[40]  Nathalie Japkowicz,et al.  Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks , 2004, Machine Learning.

[41]  Joshua Alspector,et al.  Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking , 2004, Information Retrieval.

[42]  Joshua Alspector,et al.  Data duplication: an imbalance problem ? , 2003 .

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

[44]  Robert P. W. Duin,et al.  Uncertainty sampling methods for one-class classifiers , 2003 .

[45]  R. Srihari,et al.  Optimally Combining Positive and Negative Features for Text Categorization , 2003 .

[46]  Edward Y. Chang,et al.  Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .

[47]  N. Ireland,et al.  Learning Rare Class Footprints: the REFLEX Algorithm , 2003 .

[48]  M. Maloof.  Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown , 2003 .

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

[50]  Tom Fawcett,et al.  ROC Graphs: Notes and Practical Considerations for Data Mining Researchers , 2003 .

[51]  Chris. Drummond,et al.  C 4 . 5 , Class Imbalance , and Cost Sensitivity : Why Under-Sampling beats OverSampling , 2003 .

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

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

[54]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .