Summary
Rare cases are often the most interesting cases. For example, in medical diagnosis one is typically interested in identifying relatively rare diseases, such as cancer, rather than more frequently occurring ones, such as the common cold. In this chapter we discuss the role of rare cases in Data Mining. Specific problems associated with mining rare cases are discussed, followed by a description of methods for addressing these problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agarwal, R., Imielinski, T., Swami, A. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data; 1993.
Ali, K., Pazzani, M. HYDRA-MM: learning multiple descriptions to improve classification accuracy. International Journal of Artificial Intelligence Tools 1995; 4.
Carvalho, D. R., Freitas, A. A. A genetic algorithm for discovering small-disjunct rules in Data Mining. Applied Soft Computing 2002, 2(2):75-88.
Carvalho, D. R., Freitas, A. A. New results for a hybrid decision tree/genetic algorithm for Data Mining. Proceedings of the Fourth International Conference on Recent Advances in Soft Computing; 2002.
Freitas, A. A. Evolutionary computation. In Handbook of Data Mining and Knowledge Discovery; Oxford University Press, 2002.
Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
Holte, R. C., Acker, L. E., Porter, B.W. Concept learning and the problem of small disjuncts. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence; 1989.
Japkowicz, N. Concept learning in the presence of between-class and within-class imbalances. Proceedings of the Fourteenth Conference of the Canadian Society for Computational Studies of Intelligence, Springer-Verlag; 2001.
Japkowicz, N. Supervised learning with unsupervised output separation. International Conference on Artificial Intelligence and Soft Computing; 2002.
Japkowicz, N., Stephen, S. The class imbalance problem: a systematic study. Intelligent Data Analysis 2002; 6(5):429-450.
Joshi, M. V., Agarwal, R. C., Kumar, V. Mining needles in a haystack: classifying rare classes via two-phase rule induction. SIGMOD ’01 Conference on Management of Data; 2001.
Joshi, M. V., Kumar, V., Agarwal, R. C. Evaluating boosting algorithms to classify rare cases: comparison and improvements. First IEEE International Conference on Data Mining; 2001.
Joshi, M. V., Agarwal, R. C., Kumar, V. Predicting rare classes: can boosting make any weak learner strong? Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2002.
Kubat, M., Holte, R. C., Matwin, S. Machine learning for the detection of oil spills in satellite radar images. Machine Learning 1998; 30(2):195-215.
Liu, B., Hsu,W., Ma, Y. Mining association rules with multiple minimum supports. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 1999.
Riddle, P., Segal, R., Etzioni, O. Representation design and brute-force induction in a Boeing manufacturing design. Applied Artificial Intelligence 1994; 8:125-147.
Rokach L. and Maimon O., Data mining for improving the quality of manufacturing: A feature set decomposition approach. Journal of Intelligent Manufacturing 17(3): 285299,2006.
Schapire, R. E. A brief introduction to boosting. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
Ting, K. M. The problem of small disjuncts: its remedy in decision trees. Proceeding of the Tenth Canadian Conference on Artificial Intelligence; 1994.
Van den Bosch, A., Weijters, T., Van den Herik, H. J., Daelemans, W. When small disjuncts abound, try lazy learning: A case study. Proceedings of the Seventh Belgian-Dutch Conference on Machine Learning; 1997.
Weiss, G. M. Learning with rare cases and small disjuncts. Proceedings of the Twelfth International Conference on Machine Learning; Morgan Kaufmann, 1995.
Weiss, G. M., Hirsh, H. Learning to predict rare events in event sequences. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining; 1998.
Weiss, G. M. Timeweaver: a genetic algorithm for identifying predictive patterns in sequences of events. Proceedings of the Genetic and Evolutionary Computation Conference; Morgan Kaufmann, 1999.
Weiss, G. M., Hirsh, H. A quantitative study of small disjuncts. Proceedings of the Seventeenth National Conference on Artificial Intelligence; AAAI Press, 2000.
Weiss, G. M. Mining with Rarity—Problems and Solutions: A Unifying Framework. SIGKDD Explorations 2004: 6(1):7-19.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Weiss, G.M. (2009). Mining with Rare Cases. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_38
Download citation
DOI: https://doi.org/10.1007/978-0-387-09823-4_38
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-09822-7
Online ISBN: 978-0-387-09823-4
eBook Packages: Computer ScienceComputer Science (R0)