| Summary: | 碩士 === 國立交通大學 === 科技管理研究所 === 105 === Financial Supervisory Commission provided the evidence showing a stable growth of the number of issued credit card. Therefore, there is an urgent need of a criterion to evaluate the credit risk for issuing credit cards and adjusting the credit limits. Meanwhile, IFRS9 is about to take place in 2018, the concept of Expected Credit Losses will usher the demand of a more objective and reasonable evaluation of credit default risk.
Previous researches focused more on the development and comparisons of data mining techniques surrounding the topic of model accuracy as a whole. However, compared with non-defaulters, defaulters usually take a far smaller proportion, which subjects classification models to the effect of majority class and make skewed predictions.
This research aims to provide a fair criterion of comparison among 7 classifiers, including Logistic Regression, Discriminant Analysis, K-Nearest Neighbors, Naïve Bayes, Artificial Neural Network, Random Forest, and Support Vector Machine. After evaluating their performances under data imbalance property, the imbalanced data will be made balanced with Synthetic Minority Oversampling Technique and Tomek Links and fed into 7 classifiers again for further performance evaluation. Finally the suggestions for appropriate classifiers under different situations will be provided based on the research result.
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