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題名：結合基因演算法最佳化「支持向量機」參數∼財務危機上之應用
作者：吳智鴻
作者(外文)：Wu, Chih-Hung
校院名稱：國立臺北大學
系所名稱：企業管理學系
指導教授：古永嘉
方文昌
學位類別：博士
出版日期：2003
主題關鍵詞：支援向量機；基因演算法；財務危機預測；Support Vector Machine；Genetic Algorithm；Financial Distress Prediction
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支持向量機（SVM）為目前在機器學習領域中十分受到注目的一種模式。然而在使用此模式時必須先決定其參數的數值，方能使模式的結果最佳化。但是目前很少研究針對如何將SVM的參數最佳化的議題進行研究。因此本研究的目的在於提出一新的模式（GA-SVM），此模式結合SVM與實數型基因演算法（RGA），能同時最佳化SVM的兩個參數（C 與 σ），藉此能提高SVM的預測準確度與外部效度。此外，本研究將所提出之創新模式（GA-SVM）應用於預測台灣之財務危機公司，並同時與其他模式（MDA, logit, probit、NN、SVM)之預測準確度進行比較。
研究結果發現SVM的最佳化參數值並非一常數，其值隨著資料特性的不同，而有不同的最佳化區間值。將SVM的參數值最佳化後，可顯著提高SVM模式之預測財務危機公司的準確度。而本研究所提出的GA-SVM模式，在所有的模式中，不論是在哪個樣本，均具有最高的預測力，換言之，具有最佳的內部效度與外部效度。此外，大多數的財務比率均拒絕常態性的假設，也因此造成多變量統計模式在財務危機的預測準確度不佳，尤其是多變量區別分析（MDA）之預測準確度最差。本研究同時亦分析了變數篩選對財務危機公司預測正確率的影響，在「全部變數投入」與透過「MWW變數篩選法」下，此兩種變數篩選法所建立之財務危機預警模式，其預測準確度相差不大。本研究結果展現了SVM與RGA兩者結合所建立之Hybrid System的極佳應用性，在後續的研究中可拓展此模式的應用方向，將此模式應用於其他的領域之中（尤其是財務領域）。
The support vector machine (SVM) has become one of the most promising and popular learning machines. However, it is difficult to constructing a highly effective model for predicting before the parameters of SVM have been carefully determined (Duan, Keerthi, and Poo, 2002). Two parameters, C and σ, must be carefully predetermined in establishing an efficient SVM model. The first parameter, C, determines the trade-off between the minimization of the fitting error and the maximization of smoothness. The second parameter, σ, specifies the bandwidth of the Radial Basis Function (RBF) kernel.
Therefore, the aim of this study is to develop a model that can automatically determine the optimal parameters, C and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. The real-valued genetic algorithm (RGA) was adapted to SVM to determine the optimal values of SVM parameters to increase the accuracy of its predictions. Additionally, the model (GA-SVM) was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN, SVM).
This study found that SVM has different optimal values of parameters in various dataset as determined by applying the RGA. The optimal values fall into a non-uniform range. Precisely predetermining the values of SVM parameters drastically increased the accuracy of predictions of bankruptcy. A comparison of predictive accuracy reveals that the artificial intelligent models (GA-SVM, SVM, and Neural Network) yield more accurate predictions in classifying financial distress firms than so the other multivariate statistical models. Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful. Regarding to the distribution of data, most of financial ratios did not support the assumption of normality the distribution of data on which multivariate statistical models especially those used in multivariate discriminant analysis (MDA) are based. Consequently, MDA exhibited the worst predictive accuracy and the largest errors in experiments. To examine the influence of variable selection on accuracy, financial ratios were used as input variables which selected by the Mann-Whitney-Wilcoxon (MWW) test in the bankruptcy models. The results reveal that the predictive accuracies of the MWW method or that obtained using all variables were similar.
Empirical results indicate that the proposed model is a very promising hybrid SVM model for predicting bankruptcy, in terms of both high predictive accuracy and generalization ability. The proposed GA-SVM model is able to automatically determine the optimal values of SVM parameters and exhibit a favorable predictive accuracy when applied to various dataset. Furthermore, the GA-SVM performed well in the holdout sample; this finding demonstrates the capability of this model to forecast the practical classification problems in other areas especially in finance.
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