A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 584)

Abstract

The main objective of this chapter is to compare a strategy of rule induction based on feature selection, exemplified by the LEM1 algorithm, with another strategy, not using feature selection, exemplified by the LEM2 algorithm. The LEM2 algorithm 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.

Keywords

Rough set theory Feature selection LERS data mining system LEM1 and LEM2 rule induction algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszówPoland

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