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research-article

Imbalanced enterprise credit evaluation with DTE-SBD

Authors:

Hui LiAuthors Info & Claims

Information Sciences—Informatics and Computer Science, Intelligent Systems, Applications: An International Journal, Volume 425, Issue C

Pages 76 - 91

https://doi.org/10.1016/j.ins.2017.10.017

Published: 01 January 2018 Publication History

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.

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Cited By

Mao C(2024)Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic setsInternational Journal of Knowledge-based and Intelligent Engineering Systems10.3233/KES-23023328:1(163-177)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/KES-230233
Ameksa MElamrani Abou Elassad ZElamrani Abou Elassad DMousannif H(2024)Predictive analysis for road accidents using a tree-based and deep learning fusion systemJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23207846:1(2381-2397)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-232078
Yao GHu XSong PZhou TZhang YYasir ALuo S(2024)AdaFNDFSInternational Journal of Intelligent Systems10.1155/2024/55298472024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/5529847
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Imbalanced enterprise credit evaluation with DTE-SBD
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 425, Issue C

January 2018

164 pages

ISSN:0020-0255

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Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 January 2018

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Cited By

Mao C(2024)Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic setsInternational Journal of Knowledge-based and Intelligent Engineering Systems10.3233/KES-23023328:1(163-177)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/KES-230233
Ameksa MElamrani Abou Elassad ZElamrani Abou Elassad DMousannif H(2024)Predictive analysis for road accidents using a tree-based and deep learning fusion systemJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23207846:1(2381-2397)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-232078
Yao GHu XSong PZhou TZhang YYasir ALuo S(2024)AdaFNDFSInternational Journal of Intelligent Systems10.1155/2024/55298472024Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1155/2024/5529847
Vaz BFigueira Á(2024)GANs in the Panorama of Synthetic Data Generation MethodsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365729421:1(1-28)Online publication date: 10-Apr-2024
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Zhang ZJi YShen JChen YZhang XYang GChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Collaborative Metapath Enhanced Corporate Default Risk Assessment on Heterogeneous GraphProceedings of the ACM Web Conference 202410.1145/3589334.3645402(446-456)Online publication date: 13-May-2024
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Xiao KJiang XHou PZhu H(2024)AutoEISInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10352661:1Online publication date: 1-Jan-2024
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Meng ZGao XZhai FLi BXiao CLi QXue BLu J(2024)An imbalanced contrastive classification method via similarity comparison within sample-neighbors with adaptive generation coefficientInformation Sciences: an International Journal10.1016/j.ins.2024.120273662:COnline publication date: 1-Mar-2024
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Zhu BWang HFan M(2024)Constructing small sample datasets with game mixed sampling and improved genetic algorithmThe Journal of Supercomputing10.1007/s11227-024-06263-x80:14(20891-20922)Online publication date: 4-Jun-2024
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Li YWang SJin JTao HHan CChen C(2024)Hybrid density-based adaptive weighted collaborative representation for imbalanced learningApplied Intelligence10.1007/s10489-024-05393-254:5(4334-4351)Online publication date: 1-Mar-2024
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Lenka SBisoy SPriyadarshini R(2024)Multiple optimized ensemble learning for high-dimensional imbalanced credit scoring datasetsKnowledge and Information Systems10.1007/s10115-024-02129-z66:9(5429-5457)Online publication date: 1-Sep-2024
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