Abstract
Genetic rule selection is an approach to the design of classifiers with high accuracy and high interpretability. It searches for a small number of simple classification rules from a large number of candidate rules. The effectiveness of genetic rule selection strongly depends on the choice of candidate rules. If we have hundreds of thousands of candidate rules, it is very difficult to efficiently search for their good subsets. On the other hand, if we have only a few candidate rules, rule selection does not make sense. In this paper, we examine the use of Pareto-optimal and near Pareto-optimal rules with respect to support and confidence as candidate rules in genetic rule selection.
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Ishibuchi, H., Kuwajima, I., Nojima, Y. (2007). Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_64
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DOI: https://doi.org/10.1007/978-3-540-74827-4_64
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