Minimal attribute space bias for attribute reduction

Authors:
Fan Min;Xianghui Du;Hang Qiu;Qihe Liu
Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
Venue:
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Year:
2007

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Abstract

Attribute reduction is an important inductive learning issue addressed by the Rough Sets society.Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains. In this paper, we propose a more reasonable strategy called the minimal attribute space bias, i.e., searching for reducts with the minimal attribute domain sizes product. In most cases, this bias can help to obtain reduced decision tables with the best space coverage, thus helpful for obtaining small rule sets with good predicting performance. Empirical study on some datasets validates our analysis.