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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29660


    Title: Feature selection in bankruptcy prediction
    Authors: Tsai,CF
    Contributors: 資訊管理研究所
    Keywords: SUPPORT VECTOR MACHINES;NEURAL-NETWORKS;GENETIC ALGORITHM;FINANCIAL RATIOS;BANK FAILURE;CLASSIFICATION;PARAMETERS;OPTIMIZATION;BUSINESS;FIRMS
    Date: 2009
    Issue Date: 2010-06-29 20:37:35 (UTC+8)
    Publisher: 中央大學
    Abstract: For many corporations, assessing the credit of investment targets and the possibility of bankruptcy is a vital issue before investment. Data mining and machine learning techniques have been applied to solve the bankruptcy prediction and credit scoring problems. As feature selection is an important step to select more representative data from a given dataset in data mining to improve the final prediction performance, it is unknown that which feature selection method is better. Therefore, this paper aims at comparing five well-known feature selection methods used in bankruptcy prediction, which are t-test, correlation matrix, stepwise regression, principle component analysis (PCA) and factor analysis (FA) to examine their prediction performance. Multi-layer perceptron (MLP) neural networks are used as the prediction model. Five related datasets are used in order to provide a reliable conclusion. Regarding the experimental results, the t-test feature selection method outperforms the other ones by the two performance measurements. (C) 2008 Elsevier B.V. All rights reserved.
    Relation: KNOWLEDGE-BASED SYSTEMS
    Appears in Collections:[資訊管理研究所] 期刊論文

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