Novel feature selection methods to Financial Distressed Prediction problem
碩士 === 國立中央大學 === 軟體工程研究所 === 99 === Variable and feature selection is an important issue in plenty of issues, especially feature sets is growing up violently. A good variable and feature selection will have bearing on performance of result. In this paper, we apply a new concept that combines expert...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/82317747052191692114 |
| Summary: | 碩士 === 國立中央大學 === 軟體工程研究所 === 99 === Variable and feature selection is an important issue in plenty of issues, especially feature sets is growing up violently. A good variable and feature selection will have bearing on performance of result.
In this paper, we apply a new concept that combines expert recommendation and machine learning algorithm to create a novel feature selection, and utilize the financial distress prediction problem as a study case to prove our idea.
We apply two methods that Advanced wrapper method & mixed of expert and machine (MEM) to applicate in nonstructed business problem and believe this proposed methods be better performance than original methods included predictor accuracy and few feature set.
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