| Summary: | 碩士 === 國立中正大學 === 會計與資訊科技研究所 === 96 === In recent years, information globalization makes a great change on the business
operation environment, and the global economic declines which enhance the probability of
financial distress. Business bankruptcy will significant affect the economical frame of a
country and make huge damage. The purpose of this thesis is to build up a meta-learning
classifier by data mining and machine learning techniques, and to compare with single
classifiers and stacked generalization in order to establish a more effective bankruptcy
prediction model. In particular, the first level classifiers aims at filtering out
unrepresentative training data (or outliers) for the second level (meta) classifier. In this
thesis, three classification (or supervised learning) techniques are considered, which are
neural network (MLP), decision tree (CART) and logistic regression (LR). These three
classifiers are used to build up single baseline classifiers, the stacked generalization
classifier, and the meta-learning classifier respectively. The comparative results of the
Baseline, Stacked generalization and Meta-learning approaches show that the
Meta-learning approach perform the best in terms of prediction accuracy as well as the
Type I and II errors.
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