Classification Using Multiple and Negative Target Rules
Rules are a type of human-understandable knowledge, and rule-based methods are very popular in building decision support systems. However, most current rule based classification systems build small classifiers where no rules account for exceptional instances and a default prediction plays a major role in the prediction. In this paper, we discuss two schemes to build rule based classifiers using multiple and negative target rules. In such schemes, negative rules pick up exceptional instances and multiple rules provide alternative predictions. The default prediction is removed and hence all predictions relate to rules providing explanations for the predictions. One risk for building a large rule based classifier is that it may overfit training data and results in low predictive accuracy. We show experimentally that one classifier is more accurate than a benchmark rule based classifier, C4.5rules.
Keywordsclassification association rule negative and multiple rule
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- 1.Blake, E.K.C., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
- 2.Clark, P., Boswell, R.: Rule induction with CN2: Some recent improvements. In: Machine Learning - EWSL 1991, pp. 151–163 (1991)Google Scholar
- 3.Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3(4), 261–283 (1989)Google Scholar
- 5.Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings 2001 IEEE International Conference on Data Mining (ICDM 2001), pp. 369–376. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
- 6.Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 27–31 (1998)Google Scholar
- 7.Liu, B., Ma, Y., Wong, C.: Improving an association rule based classifier. In: 4th European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD, pp. 504–509 (2000)Google Scholar
- 8.Michalski, R., Mozetic, I., Hong, J., Lavrac, N.: The AQ15 inductive learning system: an overview and experiments. In: Proceedings of IMAL 1986, Orsay, Université de Paris-Sud (1986)Google Scholar
- 9.Quinlan, J.R.: C4.5. In: Programs for Machine Learning, Morgan Kaufmann, San Mateo (1993)Google Scholar