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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 statistica...
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ndltd-TW-106NCU053960662019-10-31T05:22:24Z http://ndltd.ncl.edu.tw/handle/gtubgf none 正規化與變數篩選在破產領域的適用性研究 Hsing-Wei Huang 黃星瑋 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. 蘇坤良 2018 學位論文 ; thesis 88 zh-TW |
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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.
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蘇坤良 |
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蘇坤良 Hsing-Wei Huang 黃星瑋 |
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Hsing-Wei Huang 黃星瑋 |
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Hsing-Wei Huang 黃星瑋 none |
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Hsing-Wei Huang |
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2018 |
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http://ndltd.ncl.edu.tw/handle/gtubgf |
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AT hsingweihuang none AT huángxīngwěi none AT hsingweihuang zhèngguīhuàyǔbiànshùshāixuǎnzàipòchǎnlǐngyùdeshìyòngxìngyánjiū AT huángxīngwěi zhèngguīhuàyǔbiànshùshāixuǎnzàipòchǎnlǐngyùdeshìyòngxìngyánjiū |
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