A Hellinger‐Based Importance Measure of Association Rules for Classification Learning. Issue 9 (13th May 2014)

• Record Type: Journal Article
• Authors: Lee, Chang‐Hwan
• Is Part Of: International journal of intelligent systems. Volume 29:Issue 9(2014:Sep.)
• Page Start: 807
• Page End: 822
• Publication Date: 2014-05-13
• Subjects: Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.21664
• Languages: English
• ISSNs: 0884-8173

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>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 <italic>HD</italic> 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.</p> </abstract>