在目前眾多的研究議題中，特徵選取(Variable and feature selection)已經是一個越來越令人關注的議題。尤其是當我們收集樣本的特徵集(Feature sets)成千上百的增加的時候，一個好的特徵選取方法可以使得結果令人滿意。 本論文提出了一個概念，此概念是嘗詴去結合專家意見(Expert recommendation)與機器學習演算法(Machine Learning Algorithm)後，創造出一種混合型的特徵選取方法(Novel feature selection methods)，並且使用預測財務危機公司(Financial Distressed Prediction，簡稱FDP)此問題當作案例做為實驗證實。 本論文的貢獻在於對於特徵選取這個議題而言，我們提供了兩個新的方法：Advanced wrapper method & Mix of Expert and Machine(MEM)。而這兩個方法對於應用在非結構化的商業問題上(unstructured nature of the business problems)有著比貣以往的方法更佳的結果-擁有更勝於以往的預測準確率以及為數少量的推薦特徵集。 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.
