| Summary: | 碩士 === 國立虎尾科技大學 === 資訊管理研究所在職專班 === 97 === In recent years, a number of financial crises have made prediction of resuming stocks requiring full delivery to normal trades to become a noticeable topic to both practices and academy. In order to make their decisions correctly in time, all of the creditors, analysts, investors and regulators wish to predict whether financially distressed firms will be able to emerge based on the information available at the time of the company’s stocks requiring full delivery. However, evaluating the feasibility of financial reorganization success is complex. In this research, we employed decision tree-based mining techniques to develop a prediction model. Besides, the multi-learner model constructed by boosting ensemble approach with decision tree algorithm is used to enhance the prediction accuracy rate. The empirical results show that the classification accuracy has been improved by using multi-leaner model in terms of less Type II errors. In particular, the extracted rules from the data mining approach can be developed as a computer model for the prediction and like expert systems.
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