摘要
针对传统粗糙集在关键信息缺省较多,存在无法进行精确约简和分类等弊端,将贝叶斯原理引入到粗糙集理论中,利用贝叶斯分类器具有概率性语义和因果关系的优点,以及将先验知识和概率相结合的特点,探索和构建了规则支持度、置信因子、覆盖因子的规则获取算法,并通过实例对该算法进行说明和分析.
In view of such defects as the default of the key information, incapability of precision reduction and classification in the traditional rough set, we introduced the Bayes theory into rough set theory. By making use of the advantages of probabilistic semantics, causality and the combination of prior knowledge and probability in Bayes theory, we explored and constructed the rule acquisition algorithm of rule support, certain factor and coverage factor. The algorithm is illustrated and analyzed through some examples in the paper.
出处
《南昌工程学院学报》
CAS
2010年第3期1-4,共4页
Journal of Nanchang Institute of Technology
关键词
贝叶斯理论
粗糙集
规则获取
决策算法
Bayes theory
rough set
rule acquisition
decision algorithm
