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논문 기본 정보
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- 학술저널
- 저자정보
- 발행연도
- 2016.4
- 수록면
- 63 - 71 (9page)
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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.
목차
Abstract
I. Introduction
II. Preliminaries
III. The Proposed Scheme and Analysis Result
IV. Concluding Remarks
REFERENCES
참고문헌 (17)
참고문헌 신청
Min-Seo Kim, Joon-Whan Oh, "Discretionary Accruals` Relation to Credit Score and Financial Score, Korean Accounting Journal, No.22, pp.105-131, 2013.
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.
Hall, M. A, "Correlation-based feature selection for machine learning", Doctoral dissertation, The University of Waikato, 1999.
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.
Witten, I. H., and Frank, E., Data Mining: ractical machine learning toolsand techniques, Morgan Kaufmann, 2005.
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.
Hall, M. A, "Correlation-based feature selection for machine learning", Doctoral dissertation, The University of Waikato, 1999.
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.
Witten, I. H., and Frank, E., Data Mining: ractical machine learning toolsand techniques, Morgan Kaufmann, 2005.
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UCI(KEPA) : I410-ECN-0101-2016-325-002841442