关键词: 粗糙集, 遗传算法, 区分矩阵, 属性约简, k近邻算法

Abstract: The prerequisite for reduction is to guarantee the classification capacity of the knowledge base invariant. Thus, the weak reduction is defined. Taking advantage of discernable matrixes which can calculate the weak reduction easily and genetic algorithms in global optimization, this paper regards the chromosome coverage of the discrimination function as the fitness function parameters, and an attribute reduction algorithm based on genetic algorithms and discernable matrixes is proposed. In the algorithm, rough set decision tables based on partition and covering is researched by measuring granularity again. The weak reduction effectiveness is evaluated through k-nearest neighbor accuracy. The validity of the algorithm is proved by a UCI data set. The time complexity of the algorithm is polynomial.

Key words: rough sets, genetic algorithm, discernable matrix, attribute reduction, k-nearest neighbor