关键词: 最近邻规则, 细胞自动机, 约减, 一致性子集

Abstract: Most of current nearest neighbor rule condensation algorithms only guarantee the accuracy of classifier and the number of condensed rules, don’t consider the efficiency and generalization capability. This paper presents cellular automata (CA) based nearest neighbor rule condensation method to reduce useless points in a given training set. The method remains only the points on the boundary between different classes and the amount of condensed rules of the reference set can be revised by the granularity of the cellular automata lattice. The main advantages of the proposed method are, that it is able to condense a given rule set within less time when the number of the rules in a given rule set is very large, and can get a consistent reference set of the given set in an iterative or accumulated manner. This paper tests the method using 13 different datasets. The experiments show successful results when the size of the given dataset is very large.

Key words: nearest neighbor rule, cellular automata, condensation, consistent subset