Fuzzy Rough Set and Information Entropy Based Feature Selection for Credit Scoring

2009 ◽  
Author(s):  
Ping Yao
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Information Entropy ◽  
Credit Scoring ◽  
Fuzzy Rough Set ◽  
Selection For

scholarly journals Rough set and Tabu search based feature selection for credit scoring

2010 ◽  
Vol 1 (1) ◽  
pp. 2425-2432 ◽  
Author(s):  
Jue Wang ◽  
Kun Guo ◽  
Shouyang Wang
Keyword(s):  
Feature Selection ◽  
Tabu Search ◽  
Rough Set ◽  
Credit Scoring ◽  
Selection For

Rough set and scatter search metaheuristic based feature selection for credit scoring

2012 ◽  
Vol 39 (6) ◽  
pp. 6123-6128 ◽  
Author(s):  
Jue Wang ◽  
Abdel-Rahman Hedar ◽  
Shouyang Wang ◽  
Jian Ma
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Credit Scoring ◽  
Scatter Search ◽  
Selection For

Active Incremental Feature Selection Using a Fuzzy-Rough-Set-Based Information Entropy

2020 ◽  
Vol 28 (5) ◽  
pp. 901-915 ◽  
Author(s):  
Xiao Zhang ◽  
Changlin Mei ◽  
Degang Chen ◽  
Yanyan Yang ◽  
Jinhai Li
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Information Entropy ◽  
Fuzzy Rough Set ◽  
Incremental Feature Selection

scholarly journals A Fast Feature Selection Algorithm by Accelerating Computation of Fuzzy Rough Set-Based Information Entropy

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 788 ◽  
Author(s):  
Xiao Zhang ◽  
Xia Liu ◽  
Yanyan Yang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Fast Algorithm ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Feature Subset ◽  
Fuzzy Rough Set ◽  
Effective Measure

The information entropy developed by Shannon is an effective measure of uncertainty in data, and the rough set theory is a useful tool of computer applications to deal with vagueness and uncertainty data circumstances. At present, the information entropy has been extensively applied in the rough set theory, and different information entropy models have also been proposed in rough sets. In this paper, based on the existing feature selection method by using a fuzzy rough set-based information entropy, a corresponding fast algorithm is provided to achieve efficient implementation, in which the fuzzy rough set-based information entropy taking as the evaluation measure for selecting features is computed by an improved mechanism with lower complexity. The essence of the acceleration algorithm is to use iterative reduced instances to compute the lambda-conditional entropy. Numerical experiments are further conducted to show the performance of the proposed fast algorithm, and the results demonstrate that the algorithm acquires the same feature subset to its original counterpart, but with significantly less time.

Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy

2016 ◽  
Vol 56 ◽  
pp. 1-15 ◽  
Author(s):  
Xiao Zhang ◽  
Changlin Mei ◽  
Degang Chen ◽  
Jinhai Li
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Information Entropy ◽  
Mixed Data ◽  
Fuzzy Rough Set
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