A Hellinger-Based Importance Measure of Association Rules for Classification Learning
  • Lee, Chang-Hwan
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초록

Classification learning with association rules has been an active research area during recent years. Thus, it is important to establish some numerical importance measure for association rules. In this paper, we propose a new rule importance measure, called a HD measure, using information theory. A num ber of properties of the new measure are analyzed, and its classification performances are compared with that of other rule measures. (C) 2014 Wiley Periodicals, Inc.

제목
A Hellinger-Based Importance Measure of Association Rules for Classification Learning
저자
Lee, Chang-Hwan
DOI
10.1002/int.21664
발행일
2014-09
유형
Article
저널명
International Journal of Intelligent Systems
29
9
페이지
807 ~ 822