基于可变多粒度概率粗糙集的分类模型

Classification Model Based on Variable Multi-granulation Probabilistic Rough Set

以多粒度粗糙集理论为背景,结合可变多粒度思想与错误分类率思想,提出可变多粒度概率粗糙集(VMGPRS)模型.结合粗糙集理论中的属性约简思想,提出粒度约简算法,发现并解决可变多粒度模型中由于参数设定而引发的约简后粒度冗余问题.将约简前后的数据应用于SVM、KNN、NB等经典分类算法,验证约简对数据的分类能力几乎无影响.将规则与算法结合,设计基于规则的分类算法,并且实验分析VMGPRS模型中的2个调节参数α、β对分类器分类效果的影响.

Based on the multi-granulation rough set theory, a variable multi-granulation probabilistic rough set （VMGPRS） model combining the ideas of variable multi-granulation and misclassification rate is proposed. A granulation reduction algorithm is put forward grounded on the concept of attribute reduction in rough set theory, and the granulation redundancy caused by parameter setting in the variable multi- granulation rough set model is found and solved. The data before and after the reduction are applied to classical classification algorithms such as support vector machine, k-nearest neighbor, Naive Bayes, and it is verified that the classification ability of data is hardly influenced by the reduction. With the combination of the rule and the proposed algorithm, a rule-based classification algorithm is designed. Furthermore, two adjustment parameters, α and β, in the VMGPRS model are analyzed for classification effect of the classifier.