Three-way decision reduction in neighborhood systems |
| |
Affiliation: | 1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, PR China;2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, PR China;1. School of Mathematics, Northwest University, Xi’an 710069, PR China;2. College of Science, Xi’an Shiyou University, Xi’an 710065, PR China;3. School of Computer Science and Technology, Xidian University, Xi’an 710071, PR China |
| |
Abstract: | Rough set reduction has been used as an important preprocessing tool for pattern recognition, machine learning and data mining. As the classical Pawlak rough sets can just be used to evaluate categorical features, a neighborhood rough set model is introduced to deal with numerical data sets. Three-way decision theory proposed by Yao comes from Pawlak rough sets and probability rough sets for trading off different types of classification error in order to obtain a minimum cost ternary classifier. In this paper, we discuss reduction questions based on three-way decisions and neighborhood rough sets. First, the three-way decision reducts of positive region preservation, boundary region preservation and negative region preservation are introduced into the neighborhood rough set model. Second, three condition entropy measures are constructed based on three-way decision regions by considering variants of neighborhood classes. The monotonic principles of entropy measures are proved, from which we can obtain the heuristic reduction algorithms in neighborhood systems. Finally, the experimental results show that the three-way decision reduction approaches are effective feature selection techniques for addressing numerical data sets. |
| |
Keywords: | Rough set theory Three-way decisions Attribute reduction Neighborhood systems |
本文献已被 ScienceDirect 等数据库收录! |
|