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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52577

Title: Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates
Authors: Jie Sun
Jie Lang
Hamido Fujita
Hui Li
Contributors: 國立臺灣海洋大學:資訊工程學系
Keywords: Enterprise credit evaluation
Decision tree ensemble
SMOTE
Bagging
Differentiated sampling rates
Imbalanced classification
Date: 2018-01
Issue Date: 2019-11-19T01:48:10Z
Publisher: Information Sciences
Abstract: Abstract: Enterprise credit evaluation model is an important tool for bank and enterprise risk management, but how to construct an effective decision tree (DT) ensemble model for imbalanced enterprise credit evaluation is seldom studied. This paper proposes a new DT ensemble model for imbalanced enterprise credit evaluation based on the synthetic minority over-sampling technique (SMOTE) and the Bagging ensemble learning algorithm with differentiated sampling rates (DSR), which is named as DTE-SBD (Decision Tree Ensemble based on SMOTE, Bagging and DSR). In different times of iteration for base DT classifier training, new positive (high risky) samples are produced to different degrees by SMOTE with DSR, and different numbers of negative (low risky) samples are drawn with replacement by Bagging with DSR. However, in the same time of iteration with certain sampling rate, the training positive samples including the original and the new are of the same number as the drawn training negative samples, and they are combined to train a DT base classifier. Therefore, DTE-SBD can not only dispose the class imbalance problem of enterprise credit evaluation, but also increase the diversity of base classifiers for DT ensemble. Empirical experiment is carried out for 100 times with the financial data of 552 Chinese listed companies, and the performance of imbalanced enterprise credit evaluation is compared among the six models of pure DT, over-sampling DT, over-under-sampling DT, SMOTE DT, Bagging DT, and DTE-SBD. The experimental results indicate that DTE-SBD significantly outperforms the other five models and is effective for imbalanced enterprise credit evaluation.
Relation: 425 pp.76-91
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52577
Appears in Collections:[資訊工程學系] 期刊論文

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