Comparison of Feature Selection Methods for Credit Risk Assessment

Sri Vasavi College, Erode Self-Finance Wing 3rd February 2017 National Conference on Computer and Communication NCCC’17

Format: Volume 5, Issue 1, No 5, 2017

Copyright: All Rights Reserved ©2017

Year of Publication: 2017

Author: G. Arutjothi,Dr.C.Senthamarai

Reference:IJCS-179

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Abstract

Fund is the greatest variable of the Banking Industry. In Banking Industry achievement and disappointment depends on the credit. Keeping money Industries are focused today with increment in volume, speed and assortment of new and existing information. Managing and analyzing the massive data is more difficult. The credit scoring databases are often large and characterized by redundant and irrelevant features. With this features, classification methods become more difficult. This difficulty can be solved by using feature selection methods. The main objective of the feature selection is to reduce the size of dimensions, costs and increase the classification accuracy. This research paper uses a filter feature selection model for finding the optimal feature subset to evaluate the credit risk. The filter model is implemented using WEKA tool. Comparison study is made to find the credit risk assessment.

References

A. Mehdi Naseriparsa, Amir, Touraj, “A Hybrid Feature Selection Method to Improve Performance of a Group of Classification Algorithm” , International Journal of Computer Applications, Volume 69, No.17,May 2013. B. Vipin Kumar and Sonajhania minz, “ Feature selection: A Literature Review” , Smart Computing Review, Volume 4, no.3, June 2014.C. Silvia Cateni, Colla and Vannucci, “ A Hybrid Feature Selection Method for Classification Purposes”, 8th European Modeling Symposium, IEEE, 2014. D. Janecek, G.K, Gansteres, N, Demel, A and Euker, “ On the Relationship between Feature Selection and Classification Accuracy” Journal of Machine Learning and research, Workshop and Conference Proceedings 4, P 90-105. E. Cheng, Li,Cho and Chen-hong, “ A Hybrid Feature Selection Method for Microarray Classification”, International Journal of Computer Science, 2008. F. M.Dash, H.Liu, “ Feature Selection for Classification”, Intelligent Data Analysis, p.131-156, 1997. G. http://mlr.cs.umass.edu/ml/datasets.htmlhttp://mlr.cs.umass.edu/ml/datasets.html


Keywords

Classification, Data Mining, Credit Risk, Feature Selection, Filter .

This work is licensed under a Creative Commons Attribution 3.0 Unported License.   

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