Mining with rarity: a unifying framework
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[1] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[2] Robert C. Holte,et al. Concept Learning and the Problem of Small Disjuncts , 1989, IJCAI.
[3] D. E. Goldberg,et al. Genetic Algorithms in Search , 1989 .
[4] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[5] Tomasz Imielinski,et al. Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.
[6] Michael J. Pazzani,et al. Reducing Misclassification Costs , 1994, ICML.
[7] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[8] Oren Etzioni,et al. Representation design and brute-force induction in a Boeing manufacturing domain , 1994, Appl. Artif. Intell..
[9] Michael J. Pazzani,et al. Hydra-mm: Learning Multiple Descriptions to Improve Classification Accuracy , 1995, Int. J. Artif. Intell. Tools.
[10] Nathalie Japkowicz,et al. A Novelty Detection Approach to Classification , 1995, IJCAI.
[11] Gary M. Weiss. Learning with Rare Cases and Small Disjuncts , 1995, ICML.
[12] William W. Cohen. Fast Effective Rule Induction , 1995, ICML.
[13] Ron Kohavi,et al. Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.
[14] Andrew P. Bradley,et al. The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..
[15] Antal van den Bosch,et al. When small disjuncts abound, try lazy learning: A case study , 1997 .
[16] Claire Cardie,et al. Improving Minority Class Prediction Using Case-Specific Feature Weights , 1997, ICML.
[17] Stan Matwin,et al. Learning When Negative Examples Abound , 1997, ECML.
[18] Stan Matwin,et al. Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.
[19] Charles X. Ling,et al. Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.
[20] Ron Kohavi,et al. Data Mining with MineSet: What Worked, What Did Not, and What Might , 1998 .
[21] 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.
[22] Haym Hirsh,et al. Learning to Predict Rare Events in Event Sequences , 1998, KDD.
[23] Robert E. Schapire,et al. A Brief Introduction to Boosting , 1999, IJCAI.
[24] Wynne Hsu,et al. Mining association rules with multiple minimum supports , 1999, KDD '99.
[25] Gary M. Weiss. Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events , 1999 .
[26] Salvatore J. Stolfo,et al. AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.
[27] Robert C. Holte,et al. Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria , 2000, ICML.
[28] Haym Hirsh,et al. A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.
[29] Vipin Kumar,et al. Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[30] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[31] Nathalie Japkowicz,et al. A Mixture-of-Experts Framework for Learning from Imbalanced Data Sets , 2001, IDA.
[32] Nathalie Japkowicz,et al. Concept-Learning in the Presence of Between-Class and Within-Class Imbalances , 2001, Canadian Conference on AI.
[33] Vipin Kumar,et al. Mining needle in a haystack: classifying rare classes via two-phase rule induction , 2001, SIGMOD '01.
[34] Bianca Zadrozny,et al. Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.
[35] JapkowiczNathalie,et al. The class imbalance problem: A systematic study , 2002 .
[36] Deborah R. Carvalho,et al. A genetic-algorithm for discovering small-disjunct rules in data mining , 2002, Appl. Soft Comput..
[37] Nathalie Japkowicz,et al. Supervised Learning with Unsupervised Output Separation , 2002 .
[38] Vipin Kumar,et al. Predicting rare classes: can boosting make any weak learner strong? , 2002, KDD.
[39] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[40] Nathalie Japkowicz,et al. The class imbalance problem: A systematic study , 2002, Intell. Data Anal..
[41] Robert C. Holte,et al. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .
[42] Nitesh V. Chawla,et al. C4.5 and Imbalanced Data sets: Investigating the eect of sampling method, probabilistic estimate, and decision tree structure , 2003 .
[43] Rong Yan,et al. On predicting rare classes with SVM ensembles in scene classification , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[44] Nitesh V. Chawla,et al. SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.
[45] Foster J. Provost,et al. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..
[46] Tom Fawcett,et al. Robust Classification for Imprecise Environments , 2000, Machine Learning.
[47] Pedro M. Domingos,et al. Tree Induction for Probability-Based Ranking , 2003, Machine Learning.
[48] Adam Kowalczyk,et al. Extreme re-balancing for SVMs: a case study , 2004, SKDD.
[49] Stan Matwin,et al. Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.
[50] Alex A. Freitas,et al. New Results for a Hybrid Decision Tree/Genetic Algorithm for Data Mining , 2004 .
[51] Jerzy W. Grzymala-Busse,et al. An Approach to Imbalanced Data Sets Based on Changing Rule Strength , 2004, Rough-Neural Computing: Techniques for Computing with Words.
[52] J. Ross Quinlan. Improved Estimates for the Accuracy of Small Disjuncts , 2005, Machine Learning.