English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6498/11670
Visitors : 27710430      Online Users : 204
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17083

Title: Kernel Local Fisher Discriminant Analysis Based Manifold-regularized SVM Model for Financial Distress Predictions
Authors: Huang, Shian-Chang;Tang, Yu-Cheng;Lee, Chih-Wei;Chang, Ming-Jen
Contributors: 企業管理學系
Keywords: Financial distress;Dimensionality reduction;Support vector machine;Kernel local Fisher discriminant analysis;Semi-supervised learning
Date: 2012-02
Issue Date: 2013-07-11T09:05:00Z
Publisher: Elsevier Ltd.
Abstract: 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.
Relation: Expert Systems with Applications, 39(3): 3855-3861
Appears in Collections:[企業管理學系] 期刊論文

Files in This Item:

File SizeFormat

All items in NCUEIR are protected by copyright, with all rights reserved.


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback