A Hellinger-Based Importance Measure of Association Rules for Classification Learning

作者:Lee Chang Hwan*
来源:International Journal of Intelligent Systems, 2014, 29(9): 807-822.
DOI:10.1002/int.21664

摘要

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.

  • 出版日期2014-9