Title: Knowledge acquisition from many-attribute data by genetic programming with clustered terminal symbols

Authors: Akira Hara; Haruko Tanaka; Takumi Ichimura; Tetsuyuki Takahama

Addresses: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan. ' Faculty of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan. ' Faculty of Management and Information Systems, Prefectural University of Hiroshima, 1-1-71, Ujina-higashi, Minami-ku, Hiroshima 731-8558, Japan. ' Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-higashi, Asaminami-ku, Hiroshima 731-3194, Japan

Abstract: Rule extraction from database by soft computing methods is important for knowledge acquisition. For example, knowledge from the web pages can be useful for information retrieval. When genetic programming (GP) is applied to rule extraction from a database, the attributes of data are often used for the terminal symbols. However, the real databases have a large number of attributes. Therefore, the size of the terminal set increases and the search space becomes vast. For improving the search performance, we propose new methods for dealing with the large-scale terminal set. In the methods, the terminal symbols are clustered based on the similarities of the attributes. In the beginning of search, by using the clusters for terminals instead of original attributes, the number of terminal symbols can be reduced. Therefore, the search space can be reduced. In the latter stage of search, by using the original attributes for terminal symbols, the local search is performed. We applied our proposed methods to two many-attribute datasets, the classification of molecules as a benchmark problem and the page rank learning for information retrieval. By comparison with the conventional GP, the proposed methods showed the faster evolutional speed and extracted more accurate rules.

Keywords: genetic programming; knowledge acquisition; rule extraction; molecule classification; data attributes; clustering; terminal symbols; soft computing; similarities; molecules; page rank learning; information retrieval.

DOI: 10.1504/IJKWI.2012.050286

International Journal of Knowledge and Web Intelligence, 2012 Vol.3 No.2, pp.180 - 201

Published online: 04 Sep 2014 *

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