Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation

被引:2
|
作者
Guo, Yitong [1 ]
Mei, Jie [1 ]
Pan, Zhiting [1 ]
Liu, Haonan [1 ]
Li, Weiwei [1 ]
机构
[1] Northeastern Univ, Sch Business Adm, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
credit-risk evaluation; ensemble learning; imbalanced classification; diversity promotion; self-adaptive optimization; fuzzy sampling method; FEATURE-SELECTION; CLASSIFICATION; SMOTE; CLASSIFIERS; ALGORITHMS; MODEL; TREES;
D O I
10.3390/math10111790
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Ensemble learning techniques are widely applied to classification tasks such as credit-risk evaluation. As for most credit-risk evaluation scenarios in the real world, only imbalanced data are available for model construction, and the performance of ensemble models still needs to be improved. An ideal ensemble algorithm is supposed to improve diversity in an effective manner. Therefore, we provide an insight in considering an ensemble diversity-promotion method for imbalanced learning tasks. A novel ensemble structure is proposed, which combines self-adaptive optimization techniques and a diversity-promotion method (SA-DP Forest). Additional artificially constructed samples, generated by a fuzzy sampling method at each iteration, directly create diverse hypotheses and address the imbalanced classification problem while training the proposed model. Meanwhile, the self-adaptive optimization mechanism within the ensemble simultaneously balances the individual accuracy as the diversity increases. The results using the decision tree as a base classifier indicate that SA-DP Forest outperforms the comparative algorithms, as reflected by most evaluation metrics on three credit data sets and seven other imbalanced data sets. Our method is also more suitable for experimental data that are properly constructed with a series of artificial imbalance ratios on the original credit data set.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] An Empirical Evaluation of Structural Credit-Risk Models
    Tarashev, Nikola A.
    INTERNATIONAL JOURNAL OF CENTRAL BANKING, 2008, 4 (01): : 1 - 53
  • [2] Financial credit-risk evaluation with neural and neurofuzzy systems
    Piramuthu, S
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1999, 112 (02) : 310 - 321
  • [3] A novel ensemble classification model based on neural networks and a classifier optimisation technique for imbalanced credit risk evaluation
    Shen, Feng
    Zhao, Xingchao
    Li, Zhiyong
    Li, Ke
    Meng, Zhiyi
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 526
  • [4] Three-MLP Ensemble Re-RX Algorithm and Recent Classifiers for Credit-Risk Evaluation
    Hayashi, Yoichi
    Tanaka, Yuki
    Yukita, Shonosuke
    Nakano, Satoshi
    Bologna, Guido
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
  • [5] Asymmetric Random Subspace Method for Imbalanced Credit Risk Evaluation
    Wang, Gang
    SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING: THEORY AND PRACTICE, VOL 1, 2012, 114 : 1047 - 1053
  • [6] Feature selection for financial credit-risk evaluation decisions
    Piramuthu, S
    INFORMS JOURNAL ON COMPUTING, 1999, 11 (03) : 258 - 266
  • [7] An Incremental Learning Ensemble Method for Imbalanced Credit Scoring
    Tian, Jin
    Liu, Xinye
    Li, Minqiang
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 754 - 759
  • [8] Credit scoring models and credit-risk evaluation based on support vector machines
    Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Huazhong Ligong Daxue Xuebao, 2007, 5 (23-26): : 23 - 26
  • [9] Using Neural Network Rule Extraction for Credit-Risk Evaluation
    Arns Steiner, Maria Teresinha
    Steiner Neto, Pedro Jose
    Soma, Nei Yoshihiro
    Shimizu, Tamio
    Nievola, Julio Cesar
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2006, 6 (5A): : 6 - +
  • [10] A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment
    Hou, Wen-hui
    Wang, Xiao-kang
    Zhang, Hong-yu
    Wang, Jian-qiang
    Li, Lin
    KNOWLEDGE-BASED SYSTEMS, 2020, 208 (208)