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Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies -
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 Title & Authors
Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies -
Nam, Youn Chang; Lee, Kun Chang;
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 Abstract
This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.
 Keywords
credit rating system;Ordinal Logistic regression;Correlation based Feature Selection;KOSPI;
 Language
Korean
 Cited by
 References
1.
Min-Seo Kim, Joon-Whan Oh, "Discretionary Accruals' Relation to Credit Score and Financial Score, Korean Accounting Journal, No.22, pp.105-131, 2013.

2.
Jong-Il Park, Eun-Sun Ki, Soo-Young Kwon, "A Review and Some New Evidence on the Effect of Book-Tax Differences on Bond Rating", Korean Accounting Review, No.39, pp.1-55, 2014.

3.
Hall, M. A, "Correlation-based feature selection for machine learning", Doctoral dissertation, The University of Waikato, 1999.

4.
Jae-hak Yu, Han-sung Lee, Young-hee Im, Myung-Sup Kim, Dai-hee Park. "Hierarchical Internet Application Traffic Classification using a Multi-class SVM", Journal of Korean Institute of Intelligent Systems, No.20, pp.7-14, 2010.

5.
Witten, I. H., and Frank, E., Data Mining: ractical machine learning toolsand techniques, Morgan Kaufmann, 2005.

6.
Dong-Young Kim, "A Study on Effects of Corporate Social Responsibility and Credit Financial Score", The Journal of Business Education, No.28, pp.123-142, 2014.

7.
Bum-Jin Park, "The Effect of Types of Venture Capitalist and Earnings Management on Credit Rating", The Journal of Small Business Innovation, No.36, pp.179-204, 2014.

8.
Bum-Jin Park, "The Effect of Types of Venture Capitalist and Earnings Management on Credit Rating", The Journal of Small Business Innovation, No.36, pp.179-204, 2014.

9.
Moon-Tae Kim, "The Effects of Entertainment Costs on Credit Evaluation in Medical Firms", Accounting Information Review, No.33, pp.1-22, 2015.

10.
Jong-Il Park, Seong-Ho Bae, Seok-Woo Jeong, "The association between the employment of industry specialist auditors and bond rating of a client company", Korean Accounting Journal, No.22, pp.31-69, 2013

11.
Peterson, B., and Harrell Jr, F. E., "Partial proportional odds models for ordinal response variables", Applied Statistics, pp.205-217, 1990.

12.
Liu, X., "Ordinal regression analysis: Fitting the proportional odds model using Stata, SAS and SPSS", Journal of Modern Applied Statistical Methods, No.8, pp.632-645, 2009.

13.
Moon-Tae Kim, Young-Hwan Kim, "The Impacts of Foreign Ownership and Outside Directors on Bond Grading", Korean Accounting Review, No.32, pp.29-58, 2007.

14.
Sung-Yoon Ahn, "The Effect of Industrial Characteristics on the Credit Score and the Financial Score", Korean Journal of Accounting Research, No.20, pp.55-79, 2015.

15.
Sung-Ju Choi, Sang-Won Lee, " A Development of Hotel Bankruptcy Prediction Model on Artificial Neural Network", Journal of the Korea Society of Computer and Information, No.19, pp.125-133, 2014.

16.
Seok-Woo Jeong, Hyun-Ah Kim, "The Implication of the Difference between Local and Foreign Credit Rating and the Effect of Analysts", Annual Conference of Korean Accounting Association, No.1, pp.1521-1551, 2015.

17.
Rack-In Choi "A Study on An Improvement Scheme of the External Auditing System by Enforcing K-IFRS", Journal of the Korea Society of Computer and Information, No.19, pp.339-348, 2014.

18.
NICE homepage (http://www.niceinfo.co.kr/creditrating/bi_score_1_1.nice)