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• Main Authors: Hsing-Wei Huang, 黃星瑋
• Other Authors: 蘇坤良
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/gtubgf

碩士 === 國立中央大學 === 資訊管理學系 === 106 === In the field of bankruptcy prediction, it will definitely to face the class imbalance. Because in the real world, the amount of bankruptcy companies will be actually less than the non-bankruptcy companies. In the past, it was all relying on traditional statistical methods or personal intuition to determine whether to lend the money to other companies or not, but this often put the company in a crisis of bankruptcy. Many researches have begun to use machine learning to solve such problems, hoping to provide an accurate classification model for bank companies. Many scholars will indicate whether their study has normalized the bankruptcy data or not. However, no research concerned about whether normalize can improve the classification results. In our study, we make the two real data into five categories of imbalances ratios: 1,2,5,10,20 respectively. By this way, we will know the relation of imbalance ratios and normalize. Furthermore, our study will also consider about feature selection. Hopes to learn whether normalization really applies to bankruptcy prediction or not.