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Mining with Rare Cases

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Data Mining and Knowledge Discovery Handbook

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.

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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.

    Google Scholar 

  • Ali, K., Pazzani, M. HYDRA-MM: learning multiple descriptions to improve classification accuracy. International Journal of Artificial Intelligence Tools 1995; 4.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Freitas, A. A. Evolutionary computation. In Handbook of Data Mining and Knowledge Discovery; Oxford University Press, 2002.

    Google Scholar 

  • Goldberg, D. E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Japkowicz, N. Supervised learning with unsupervised output separation. International Conference on Artificial Intelligence and Soft Computing; 2002.

    Google Scholar 

  • Japkowicz, N., Stephen, S. The class imbalance problem: a systematic study. Intelligent Data Analysis 2002; 6(5):429-450.

    MATH  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Riddle, P., Segal, R., Etzioni, O. Representation design and brute-force induction in a Boeing manufacturing design. Applied Artificial Intelligence 1994; 8:125-147.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Schapire, R. E. A brief introduction to boosting. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.

    Google Scholar 

  • Ting, K. M. The problem of small disjuncts: its remedy in decision trees. Proceeding of the Tenth Canadian Conference on Artificial Intelligence; 1994.

    Google Scholar 

  • 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.

    Google Scholar 

  • Weiss, G. M. Learning with rare cases and small disjuncts. Proceedings of the Twelfth International Conference on Machine Learning; Morgan Kaufmann, 1995.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Weiss, G. M., Hirsh, H. A quantitative study of small disjuncts. Proceedings of the Seventeenth National Conference on Artificial Intelligence; AAAI Press, 2000.

    Google Scholar 

  • Weiss, G. M. Mining with Rarity—Problems and Solutions: A Unifying Framework. SIGKDD Explorations 2004: 6(1):7-19.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Fordham University, 441 East Fordham Road, Bronx, NY, 10458, USA

    Gary M. Weiss

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  1. Gary M. Weiss
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Correspondence to Gary M. Weiss .

Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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© 2009 Springer Science+Business Media, LLC

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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

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_38

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  • 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)

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