Kernelized Fuzzy Rough Sets and Their Applications

被引:151
作者
Hu, Qinghua [1 ]
Yu, Daren [1 ]
Pedrycz, Witold [2 ,3 ]
Chen, Degang [4 ]
机构
[1] Harbin Inst Technol, Harbin 150001, Peoples R China
[2] Polish Acad Sci, Inst Syst Sci, Warsaw, Poland
[3] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2M7, Canada
[4] N China Elect Power Univ, Dept Math & Phys, Beijing 102206, Peoples R China
基金
中国国家自然科学基金;
关键词
Rough set; fuzzy rough set; kernel; feature evaluation; feature selection; FEATURE-SELECTION; CLASSIFICATION; INFORMATION; REDUCTION; ALGORITHMS; MODEL; RULES;
D O I
10.1109/TKDE.2010.260
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Kernel machines and rough sets are two classes of commonly exploited learning techniques. Kernel machines enhance traditional learning algorithms by bringing opportunities to deal with nonlinear classification problems, rough sets introduce a human-focused way to deal with uncertainty in learning problems. Granulation and approximation play a pivotal role in rough sets-based learning and reasoning. However, a way how to effectively generate fuzzy granules from data has not been fully studied so far. In this study, we integrate kernel functions with fuzzy rough set models and propose two types of kernelized fuzzy rough sets. Kernel functions are employed to compute the fuzzy T-equivalence relations between samples, thus generating fuzzy information granules in the approximation space. Subsequently fuzzy granules are used to approximate the classification based on the concepts of fuzzy lower and upper approximations. Based on the models of kernelized fuzzy rough sets, we extend the measures existing in classical rough sets to evaluate the approximation quality and approximation abilities of the attributes. We discuss the relationship between these measures and feature evaluation function ReliefF, and augment the ReliefF algorithm to enhance the robustness of these proposed measures. Finally, we apply these measures to evaluate and select features for classification problems. The experimental results help quantify the performance of the KFRS.
引用
收藏
页码:1649 / 1667
页数:19
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