The Class Imbalance Problem

A Bibliography

• An, A., Cercone, N. and Huang, X. (2001), A Case Study for Learning from Imbalanced Data Sets in Advances in Artificial Intelligence: Proceedings of the 14th Conference of the Canadian Society for Computational Studies of Intelligence, pp. 1-15.
  
  
• Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall and Kevin W. Bowyer, (2003), SMOTEBoost: Improving Prediction of the Minority Class in Boosting, in 7th European Conference on Principles and Practice of Knowledge Discovery in Databases Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Pages 107 - 119
  
  
• Chawla, N.V., Bowyer, K.W., Hall, L.O. & Kegelmeyer, W.P. (2002), SMOTE: Synthetic Minority Over-sampling TEchnique , Journal of Artificial Intelligence Research (JAIR), Volume 16, pp. 321-357.
  
  
• Chawla, N., Japkowicz, N. and Kolcz, A. (editors), Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets , http://www.site.uottawa.ca/~nat/Workshop2003/workshop2003.html, August 2003.
  
  
• Chawla, N., Japkowicz, N. and Kolcz, A. (editors), SIGKDD Explorations, Special Issue on Class Imbalances , SIGKDD Explorations 6(1), June 2004.
  
  
• DeRouin, E., Brown, J., Fausett, L. & Schneider, M. (1991). Neural Network Training om Unequally Represented Classes. In Intelligent Engineering Systems Through Artificial Neural Networks, pp. 135-140.
  
  
• Domingos, Pedro (1999), Metacost: A general method for making classifiers cost sensitive, in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164.
  
  
• Drummond, Chris and Holte, Robert (2000), Explicitly Representing Expected Cost: An Alternative to ROC Representation, in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,, pp. 198-207.
  
  
• Drummond, Chris and Holte, Robert (2000), Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria, in Proceedings of the Seventeenth International Conference on Machine Learning , pp. 239-249.
  
  
• Eavis, Todd and Japkowicz, Nathalie, (2000) A Recognition-Based Alternative to Multi-Layer Perceptrons , in Advances in Artificial Intelligence: Proceedings of the 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence , pp. 280-292.
  
  
• Elkan, Charles (2001), The Foundations of Cost-Sensitive Learning , in Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence.
  
  
• Estabrooks, Andrew (2000), A Combination Scheme for Inductive Learning from Imbalanced Data Sets , MCS Thesis, Faculty of Computer Science, Dalhousie University.
  
  
• Estabrooks A., and Japkowicz, N., (2001), A Mixture-of-Experts Framework for Concept-Learning from Imbalanced Data Sets, in Proceedings of the 2001 Intelligent Data Analysis Conference.
  
  
• Estabrooks A., Jo, T., and Japkowicz, N., (2004), A Multiple Resampling Method for Learning from Imbalances Data Sets, in Computational Intelligence, Volume 20, Number 1, 2004. (in press)
  
  
• Ezawa, K., Singh, M., & Norton, S. W. (1996), Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management. In Proceedings of the International Conference on Machine Learning, ICML-96, pp. 139-147.
  
  
• Fawcett, T.E. and Provost, F., (1997), Adaptive Fraud Detection , In Data Mining and Knowledge Discovery , Volume 1, Number 3, pp. 291-316.
  
  
• Hongyu Guo and Herna L. Viktor, Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach, ACM SIGKDD Explorations, 6(1), 2004, 30-39.
  
  
• Hongyu Guo and Herna L. Viktor, Learning from Skewed Class Multi-relational Databases, Journal of Fundamenta Informaticae (FI), Special Issue on "Multi-relational Data Mining", Volume 89, Issue 1, pages 69-94, 2008
  
  
• Japkowicz, N., Myers, C. and Gluck, M. (1995), A Novelty Detection Approach to Classification , in Proceedings of the Fourteenth Joint Conference on Artificial Intelligence , pp. 518-523.
  
  
• Japkowicz, N. (editor), Proceedings of the AAAI'2000 Workshop on Learning from Imbalanced Data Sets , AAAI Tech Report WS-00-05, July 2000.
  
  
• Japkowicz, N. (2000), The Class Imbalance Problem: Significance and Strategies, in Proceedings of the 2000 International Conference on Artificial Intelligence (IC-AI'2000) , pp. 111-117.
  
  
• Japkowicz, N., (2001), Concept-Learning in the Presence of Between-Class and Within-Class Imbalances , in Advances in Artificial Intelligence: Proceedings of the 14th Conference of the Canadian Society for Computational Studies of Intelligence, pp. 67-77.
  
  
• Japkowicz, N. and Stephen, S., The Class Imbalance Problem: A Systematic Study , Intelligent Data Analysis Journal, Volume 6, Number 5, November 2002.
  
  
• Jo, T. and Japkowicz, N., Class Imbalances versus Small Disjuncts , SIGKDD Explorations 6(1), June 2004.
  
  
• Kolcz, A. and Alspector, J., Asymmetric Missing-data Problems: Overcoming the Lack of Negative Data in Preference Ranking , Information Retrieval , Volume 5, Number 1, pp. 5-40, 2002.
  
  
• Kubat, M. and Matwin, S. (1997), Addressing the Curse of Imbalanced Data Sets: One-Sided Sampling, in Proceedings of the Fourteenth International Conference on Machine Learning , pp. 179-186.
  
  
• Kubat, M., Holte R. and Matwin, S. (1997), Learning when Negative Examples Abound, in Proceedings of ECML-97 , pp. 146-153.
  
  
• Kubat M., Holte, R. and Matwin, S., (1998) Machine Learning for the Detection of Oil Spills in Satellite Radar Images , in Machine Learning , Volume 30, pp. 195-215.
  
  
• Latinne P., Saerens M. & Decaestecker C. (2001), Adjusting the outputs of a classifier to new a priori probabilities may significantly improve classification accuracy: Evidence from a multi-class problem in remote sensing. Proceedings of the 18th International Conference on Machine Learning (ICML), pp. 298-305.
  
  
• Lawrence, S., Burns, I., Back, A.D., Tsoi, A.C., Giles, C.L., (1998) Neural Network Classification and Unequal Prior Class Probabilities in G. Orr, R.-R. Muller, and R. Caruana, editors, Tricks of the Trade, Lecture Notes in Computer Science State-of-the-Art Surveys, pp. 299-314. Springer Verlag.
  
  
• Lee, Y., Wahba, G. and Ackerman, S., Classification of Satellite Radiance Data by Multicategory Support Vector Machines TR 1075, February 2003, available at http://www.stat.wisc.edu/~wahba -> TRLIST
  
  
• Lee, Y., Lin, Y. and Wahba, G., Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data , TR 1064, September 2002, available at http://www.stat.wisc.edu/~wahba -> TRLIST
  
  
• Lee, Y., Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data , TR 1063, September 2002. PhD. Thesis, available at http://www.stat.wisc.edu/~wahba -> TRLIST
  
  
• Lewis, D. and Gale, W. (1994), Training Text Classifiers by Uncertainty Sampling, in Proceedings of the Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval .
  
  
• Ling, C.X. and Li, C., (1998), Data Mining for Direct Marketing: Problems and Solutions , in Proceedings of the Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pp. 73-79,
  
  
• M. Maragoudakis, K. Kermanidis, A. Tasikas, N. Fakotakis and G. Kokkinakis, 2004, Bayesian Induction of Verb Subcategorization Frames in Imbalanced Heterogeneous Data, Journal of Quantitative Linguistics, ISSN: 0929-6174 , Swets & Zeitlinger (accepted for publication).
  
  
• Nickerson, A., Japkowicz, N. and Milios, E., (2001), Using Unsupervised Learning to Guide Resampling in Imbalanced Data Sets , in Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics.
  
  
• Nugroho, A.S., Kuroyanagi, S. and Iwata, A., (2002), A Solution for Imbalanced Training Sets Problem by CombNET-II and Its Application on Fog Forcasting , in Transactions on Information and Systems, The Institute of Electronics, Information and Communication Engineers (IEICE), Vol.E85-D, No.7, pp. 1165-1174, July 2002.
  
  
• Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T. and Brunk, C., (1994), Reducing Misclassification Costs , in Proceedings of the Eleventh International Conference on Machine Learning , pp. 217-225.
  
  
• Saerens M., Latinne P. & Decaestecker C. (2002), Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure. Neural Computation,. 14 (1), pp. 21-41.
  
  
• Riddle, P., Segal, R. and Etzioni, O., (1991), Representation Design and Brute-Force Induction in a Boeing Manufactoring Domain , in Applied Artificial Intelligence , Volume 8, pp. 125-147.
  
  
• Wahba, G., Soft and Hard Classification by Reproducing Kernel Hilbert Space Methods, TR 1067, October 2002. In Proceedings of the National Academy of Sciences, 99, 16524-16530 (2002), available at http://www.stat.wisc.edu/~wahba -> TRLIST
  
  
• Gary M. Weiss and Foster Provost (2003). "Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induction" , Journal of Artificial Intelligence Research, 19:315-354.
  
  
• Yan, L., Dodier, R., Mozer, M.C., and Wolniewicz, R., Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic. In Proceedings of the 20th International Conference on Machine Learning , 2003.
  
  
• Zadrozny, B. and Elkan, C. (2001) Learning and Making Decisions When Costs and Probabilities are Both Unknown in Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining.