NDLI: The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results

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The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results

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The multi-label OCS with a genetic algorithm for rule discovery: implementation and first results

Content Provider	ACM Digital Library
Author	Vallim, Rosane Maria Maffei Carvalho, André C.P.L.F. Duque, Thyago S.P.C. Goldberg, David E.
Abstract	Learning Classifier Systems (LCSs) are rule-based systems that can be manipulated by a genetic algorithm. LCSs were first designed by Holland to solve classification problems and a lot of effort has been made since then, resulting in a broad number of different algorithms. One of these is called Organizational Classifier System (OCS), a LCSs that tries to organize its rule set favoring good rules to be together in the same organization. However, the proposal of OCS did not include the discovery mechanism. Recently, the OCS was applied to multi-label classification, a type of classification where one instance can have more than one associated label. The authors represented the multi-label classification problem as a default hierarchy and combined the organizational capabilities of OCS together with Smith's default hierarchy formation theory to solve a simple multi-label problem. The purpose of this paper is to extend this idea with the inclusion of a genetic algorithm for the discovery of new rules and present some initial results obtained using the new method. The preliminary results obtained show that the method is comparable to other multi-label techniques. Final discussions present the conclusions of the work and some directions for further research.
Starting Page	1323
Ending Page	1330
Page Count	8
File Format	PDF
ISBN	9781605583259
DOI	10.1145/1569901.1570078
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-07-08
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multi-label classification Ga Lcs Ocs
Content Type	Text
Resource Type	Article

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