Classification Model Based on Variable Multi-granulation Probabilistic Rough Set
WANG Jiaqi, MIAO Duoqian, ZHANG Hongyun
Department of Computer Science and Technology, Tongji University, Shanghai 201804 Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804
Abstract 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.
About author:: (WANG Jiaqi, born in 1993, master student. Her research interests include rough set, natural language processing and pattern recognition.) (MIAO Duoqian(Corresponding author), born in 1964, Ph.D., professor. His research interests include rough set, granular computing, data mining and machine learning.) (ZHANG Hongyun, born in 1972, Ph.D., associate professor. Her research interests include principal curves, granular computing, rough set and data mining.)
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