An Empirical Comparison of Rule Induction Using Feature Selection with the LEM2 Algorithm

The main objective of this paper is to compare a strategy to rule induction based on feature selection with another strategy, not using feature selection, exemplified by the LEM2 algorithm. It is shown that LEM2 significantly outperforms the strategy or rule induction based on feature selection in terms of an error rate (5% significance level, two-tailed test). At the same time, the LEM2 algorithm induces smaller rule sets with the smaller total number of conditions as well.

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