Research Article | OPEN ACCESS
Fuzzified MCDM Consistent Ranking Feature Selection with Hybrid Algorithm for Credit Risk Assessment
1Y. Beulah Jeba Jaya and 2J. Jebamalar Tamilselvi
1Bharathiar University, Coimbatore
2Department of MCA, Jaya Engineering College, Chennai, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 12:1397-1403
Received: June 24, 2015 | Accepted: August 2, 2015 | Published: December 25, 2015
Abstract
Feature selection algorithms that are based on different single evaluation criterions for determining the subset of features shows varying result sets which lead to inconsistency in ranks. In contrary, Multiple Criteria Decision Making (MCDM) with Fuzzified Feature Selection methodology brings consistency in feature selection ranking with optimal features and improving the classification performance of credit risks. By adopting multiple evaluation criteria inconsistent ranks to Fuzzy Analytic Hierarchy Process (FAHP) for feature selection along with hybrid algorithm (K-Means clustering-Logistic Regression classification) results in enabling Consistent Ranking Feature Selection (CRFS) and significant improvement over classification performance measures. When the proposed methodology is used with two different credit risk data set from the UCI repository, the experimental results show that the optimal features with hybrid algorithm, indicating improvements in the performance of classification in credit risk prediction over the current existing techniques.
Keywords:
Credit risk, feature selection, fuzzy analytic hierarchy process, k-means clustering, logistic regression classification, multiple criteria decision making,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|