高可信度最小约简属性启发策略

为提高启发式算法计算最小约简的可信度,基于可辨识矩阵,研究了属性之间存在的吸收、排斥以及互斥等特征,分析其与最小约简的关联,提出了对应的最小约简属性启发策略, 建立了各个特征下属性启发策略的可信度计算模型. 在此基础上,按照可信度排序,形成了一种综合的高可信度最小约简属性启发策略,并给出了具体的约简算法. 理论和实验分析表明,本文策略具有可信度高且可信度可以估计等优点,能有效提升最小约简算法的性能.

High Confidence Heuristic Strategy for Minimal Reduction

In order to improve the confidence of minimal reducts calculated by heuristic methods, some important characters of attributes, such as absorption, repulsion, and mutex etc., are presented based on the discernibility matrix. Then the related heuristic strategies are proposed by analyzing the relation between these characters and the minimal reducts. Some confidence models of these strategies are established to order these strategies. On the basis, an integrated strategy and a related reduction algorithm are proposed to calculate a minimal redcut. Theoretic and experimental analyses show that the proposed strategies are of high confidence and effectiveness.