Integrating GA with Boosting Methods for Financial Distress Predictions

整合GA與Boosting演算法於財務危機之預測

劉昕俞(Hsin-Yu Liu) ； 黃憲彰(Shian-Chang Huang)

《品質學報》 17卷2期 (2010/04) Pp. 131-158

摘要

財務危機是公司最嚴重且最引人注意的風險。它直接影響公司的發展和生存，也可能導致資本市場的危機發生。因此，無論是在學術界或是實務界，財務危機的預測一直是個重要的議題。本研究主要有兩個階段：首先，比較資料探勘法和傳統統計方法。其次，使用Boosting演算法結合基因演算法建立一個可信且準確的危機預測模型。本研究以決策樹、邏輯斯迴歸、類神經網絡和支援向量機作為單一分類器。Boosting演算法則採用AdaboostM1、Logitboost和Multiboost。所有演算法藉由基因演算法來選擇適配的特徵變數。實證結果顯示AdaBoostM1結合基因演算法有最好的表現。

關鍵字

財務危機 ； boosting演算法 ； 基因演算法 ； AdaboostM1 ； Logitboost ； Multiboost

並列摘要

Financial distress is the most considerable and notable distress for companies. It also has a direct effect on its development and survival, and may result in a crisis in capital markets. Thus, financial distress prediction has been a critical issue in the area of academia and industry. The aims of this study are two folds: first, to compare prediction algorithms from data mining with traditional statistical methods, and second, to combine genetic algorithms (GAs) with boosting methods for developing a reliable and accurate model of bankruptcy prediction. The base classifiers we used are decision trees, logistic regressions, neural networks, and support vector machines. The boosting algorithms used are AdaboostM1, Logitboost, and Multiboost. The above algorithms are optimized by GA for input features. Empirical results indicated that integrating GA with AdaBoostM1 achieves the best performance.

並列關鍵字

financial distress ； boosting algorithm ； genetic algorithm ； AdaboostM1 ； Logitboost ； Multiboost

參考文獻

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