Using genetic algorithm for feature selection in financial distress problem

• Main Authors: OU CHIA WEN, 歐嘉文
• Other Authors: 梁德容
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/18799248978047169846

碩士 === 國立中央大學 === 軟體工程研究所 === 101 === Financial distress problem has been important and widely studied topic, development of good financial analysis model can help bank to decisions. There are two major factors, namely feature selection and classifier algorithm, influencing financial distressed prediction. Previous researches show that the forecasting accuracy is very difficult to have significant improvement by improving classification algorithm only; therefore, our research focus on the feature selection issue. Over time，we observed financial ratio growing quickly, that mean feature selection become more important, In recent years, Previous researches have shown genetic algorithm applied to feature selection in unique feature set have good performance, but we know feature size growing quickly, it is not enough to prove genetic algorithm in unique feature set. In our research, we simulate ratio growing situation, consider genetic algorithm performance. Finally, if we exclude corporate governance, we discover genetic algorithm predict performance become well when feature size larger.