@article{ART002102137},
author={남윤창 and Kun-Chang Lee},
title={Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - },
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2016},
volume={21},
number={4},
pages={63-71}
TY - JOUR
AU - 남윤창
AU - Kun-Chang Lee
TI - Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies -
JO - Journal of The Korea Society of Computer and Information
PY - 2016
VL - 21
IS - 4
PB - The Korean Society Of Computer And Information
SP - 63
EP - 71
SN - 1598-849X
AB - 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.
KW - credit rating system;Ordinal Logistic regression;Correlation based Feature Selection;KOSPI
DO -
UR -
ER -
남윤창 and Kun-Chang Lee. (2016). Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - . Journal of The Korea Society of Computer and Information, 21(4), 63-71.
남윤창 and Kun-Chang Lee. 2016, "Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - ", Journal of The Korea Society of Computer and Information, vol.21, no.4 pp.63-71.
남윤창, Kun-Chang Lee "Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - " Journal of The Korea Society of Computer and Information 21.4 pp.63-71 (2016) : 63.
남윤창, Kun-Chang Lee. Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - . 2016; 21(4), 63-71.
남윤창 and Kun-Chang Lee. "Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - " Journal of The Korea Society of Computer and Information 21, no.4 (2016) : 63-71.
남윤창; Kun-Chang Lee. Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - . Journal of The Korea Society of Computer and Information, 21(4), 63-71.
남윤창; Kun-Chang Lee. Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - . Journal of The Korea Society of Computer and Information. 2016; 21(4) 63-71.
남윤창, Kun-Chang Lee. Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - . 2016; 21(4), 63-71.
남윤창 and Kun-Chang Lee. "Analyzing empirical performance of correlation based feature selection with company credit rank score dataset - Emphasis on KOSPI manufacturing companies - " Journal of The Korea Society of Computer and Information 21, no.4 (2016) : 63-71.
