Online Classifier Considering the Importance of Attributes

Authors:
Hiroaki Ueda;Yo Nasu;Yuki Mikura;Kenichi Takahashi
Affiliations:
Graduate School of Information Sciences, Hiroshima City University,;Hitachi Electronics Services Co., Ltd.,;Graduate School of Information Sciences, Hiroshima City University,;Graduate School of Information Sciences, Hiroshima City University,
Venue:
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Year:
2008

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Abstract

We propose a new classifier ARTMAP2-AW based on adaptive resonance theory. ARTMAP2-AW evaluates the degree of importance of each attribute, and on the basis of the importance, attributes irrelevant to classification are detected for efficient learning. Experimental results show that ARTMAP2-AW acquires better classification rules than well-known classifiers.