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題名: Kernel Local Fisher Discriminant Analysis Based Manifold-regularized SVM Model for Financial Distress Predictions
作者: Huang, Shian-Chang;Tang, Yu-Cheng;Lee, Chih-Wei;Chang, Ming-Jen
貢獻者: 企業管理學系
關鍵詞: Financial distress;Dimensionality reduction;Support vector machine;Kernel local Fisher discriminant analysis;Semi-supervised learning
日期: 2012-02
上傳時間: 2013-07-11T09:05:00Z
出版者: Elsevier Ltd.
摘要: Support vector machines (SVM) have demonstrated excellent performance in numerous areas of pattern recognitions. However, traditional SVM does not make efficient use of both labeled training data and unlabeled testing data. Moreover, high dimensional and nonlinear distributed data generally degrade the performance of a classifier due to the curse of dimensionality in financial distress (or bankruptcy) predictions. To address these problems, this study proposes a novel hybrid classifier which integrates Kernel local Fisher discriminant analysis (KLFDA) with a manifold-regularized SVM (MR-SVM). KLFDA is employed to find an optimal projection which maximizes the margin between data points from different classes at each local area of data manifold, while MR-SVM data-dependently warps the structure of feature space to reflect the underlying geometry of the data manifold. Compared with other dimensionality reduction methods and conventional classifiers, the hybrid classifier performs best.
關聯: Expert Systems with Applications, 39(3): 3855-3861
顯示於類別:[企業管理學系] 期刊論文


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