CAJ | 학술논문

对于约简来说，其前提是保证知识库分类能力不变，由此引入弱约简的定义。利用区分矩阵能很容易计算出弱约简和遗传算法可以在全局寻优的优势，将染色体对区分函数的覆盖度作为适应度函数的参数，提出了一种基于遗传算法和区分矩阵的属性约简算法。算法中从粒计算的角度，重新度量粒度，对基于划分和覆盖的粗糙集决策表进行了研究。用k近邻算法通过准确率对弱约简效果进行评估。通过UCI数据集证明了该算法的有效性。该算法的时间复杂度是多项式的。
대우약간래설，기전제시보증지식고분류능력불변，유차인입약약간적정의。이용구분구진능흔용역계산출약약간화유전산법가이재전국심우적우세，장염색체대구분함수적복개도작위괄응도함수적삼수，제출료일충기우유전산법화구분구진적속성약간산법。산법중종립계산적각도，중신도량립도，대기우화분화복개적조조집결책표진행료연구。용k근린산법통과준학솔대약약간효과진행평고。통과UCI수거집증명료해산법적유효성。해산법적시간복잡도시다항식적。
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 granu-larity again. The weak reduction effectiveness is evaluated through k-nearest neighbor accuracy. The validity of the algo-rithm is proved by a UCI data set. The time complexity of the algorithm is polynomial.