Finding Essential Attributes in Binary Data

Authors:
Endre Boros;Takashi Horiyama;Toshihide Ibaraki;Kazuhisa Makino;Mutsunori Yagiura
Affiliations:
-;-;-;-;-
Venue:
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Year:
2000

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Abstract

Given a data set, consisting of n-dimensional binary vectors of positive and negative examples, a subset S of the attributes is called a support set if the positive and negative examples can be distinguished by using only the attributes in S. In this paper we consider several selection criteria for evaluating the "separation power" of supports sets, and formulate combinatorial optimization problems for finding the "best and smallest" support sets with respect to such criteria. We provide efficient heuristics, some with a guaranteed performance rate, for the solution of these problems, analyze the distribution of small support sets in random examples, and present the results of some computational experiments with the proposed algorithms