Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

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Otero, Fernando E.B. ; Freitas, Alex A. (2016)

The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules)-i.e., the ACO search is guided by the quality of a list of rules, instead of an individual rule. In this paper we propose an extension of the cAnt-MinerPB algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly-used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines and the cAnt-MinerPB producing ordered rules are also presented.
  • References (30)
    30 references, page 1 of 3

    Bacardit, J., Burke, E., and Krasnogor, N. (2009). Improving the scalability of rule-based evolutionary learning. Memetic Computing, 1(1):55-67.

    Clark, P. and Boswell, R. (1991). Rule Induction with CN2: Some Recent Improvements. In Machine Learning - Proceedings of the Fifth European Conference (EWSL-91), pages 151-163, Berlin. Springer.

    Cohen, W. (1995). Fast effective rule induction. In Proceedings of the 12th International Conference on Machine Learning, pages 115-123, San Francisco, CA, USA. Morgan Kaufmann.

    Demsˇar, J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research, 7:1-30.

    Fayyad, U., Piatetsky-Shapiro, G., and Smith, P. (1996). From data mining to knowledge discovery: an overview. In Fayyad, U., Piatetsky-Shapiro, G., Smith, P., and Uthurusamy, R., editors, Advances in Knowledge Discovery & Data Mining, pages 1-34, Cambridge, MA, USA. MIT Press.

    Franco, M., Krasnogor, N., and Bacardit, J. (2012). Post-processing Operators for Decision Lists. In Genetic and Evolutionary Computation Conference (GECCO-2012), pages 847-854, New York, NY, USA. ACM Press.

    Frank, E. and Witten, I. (1998). Generating Accurate Rule Sets Without Global Optimization. In Shavlik, J., editor, Proceedings of the Fifteenth International Conference on Machine Learning, pages 144-151, San Francisco, CA, USA. Morgan Kaufmann.

    Freitas, A. (2013). Comprehensible classification models: a position paper. ACM SIGKDD Explorations, 15(1):1-10.

    Freitas, A., Parpinelli, R., and Lopes, H. (2008). Ant colony algorithms for data classification. In Encyclopedia of Information Science and Technology, volume 1, pages 154-159. IGI Global, Hershey, PA, USA, 2nd edition.

    Garc´ıa, S. and Herrera, F. (2008). An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons. Journal of Machine Learning Research, 9:2677-2694.

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