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Credit Rating Prediction by Using Machine Learning Methods
기계 학습을 이용한 신용 등급 예측

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Authors
김도영
Advisor
최형인
Major
자연과학대학 수리과학부
Issue Date
2014-08
Publisher
서울대학교 대학원
Keywords
credit ratingmachine learninglogistic regressionneural networkssupport vector machine
Description
학위논문 (석사)-- 서울대학교 대학원 : 수리과학부, 2014. 8. 최형인.
Abstract
In this thesis, we discuss the credit ratings of companies. Our purpose is to make a credit rating prediction rule that gives each company a credit which is as correct as possible to the actual rank. We describe three representative
machine learning algorithms, which are ordinal logistic regression, neural networks and support vector machine. In addition, we try to analyze their performance and correctness and compare them to determine which method
is the most efficient in machine learning to decide ratings. We deal with two different data sets of experiments which consist of true credit rating of companies in 2009 and 2013 and financial information in the previous year.
Language
Korean
URI
http://hdl.handle.net/10371/131490
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College of Natural Sciences (자연과학대학)Dept. of Mathematical Sciences (수리과학부)Theses (Master's Degree_수리과학부)
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