A comparison of rule induction using feature selection and the LEM2 algorithm

Abstract

Themain objective of this chapter is to compare a strategy of rule induction based on feature selection, exemplified by theLEM1algorithm, with another strategy, not using feature selection, exemplified by theLEM2algorithm. TheLEM2algorithm uses all possible attribute-value pairs as the search space. It is shown that LEM2 significantly outperforms LEM1, a strategy 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. The time complexity for both algorithms is the same.