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Default Prediction by Using Machine Learning Methods
기계 학습을 이용한 부도 예측

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Authors
이아란
Advisor
최형인
Major
자연과학대학 수리과학부
Issue Date
2014-02
Publisher
서울대학교 대학원
Keywords
default predictionmachine learninglogistic regressionneural networksupport vector machineimbalanced data
Description
학위논문 (석사)-- 서울대학교 대학원 : 수리과학부, 2014. 2. 최형인.
Abstract
In this paper, we apply the machine learning methods to predict default of companies in Asian financial crisis using financial statements from 1994 to 1996. Logistic regression, neural network, and support vector machines are used to conduct the default prediction model. We use under-sampling technique and SMOTE(Synthetic Minority Over-sampling Technique) to solve the imbalanced dataset problem. Also, we compare the results with two subgroups of features, one group is from one financial statement prior to default, and the other is from three financial statements from 1994 to 1996. In addition, we discuss the performance of the machine learning methods by comparing the statistic measures.
Language
English
URI
http://hdl.handle.net/10371/131481
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College of Natural Sciences (자연과학대학)Dept. of Mathematical Sciences (수리과학부)Theses (Master's Degree_수리과학부)
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