Affiliations: Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China. [email protected] | Department of Mathematics, Chongqing Jiaotong University, Chongqing, P.R. China. [email protected] | School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang, P.R. China. [email protected] | Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China. [email protected]
Note: [] Address for correspondence: Department of Mathematics, Chongqing Jiaotong University, Chongqing, 400074, P.R. China
Abstract: The classical rough set theory is based on the conventional indiscernibility relation. It is not very good for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The valued tolerance relation is such an extended model of classical rough set theory. However, the general calculation method of tolerance degree needs to know the prior probability distribution of an information system in advance, and it is also difficult to select a suitable threshold. In this paper, a data-driven valued tolerance relation (DVT) is proposed to solve this problem based on the idea of data-driven data mining. The new calculation method of tolerance degree and the auto-selection method of threshold do not require any prior domain knowledge except the data set. Some properties about the DVT are analyzed. Experiment results show that the DVT can get better and more stable classification results than other extended models of the classical rough set theory.
