Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation
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 ide...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/11/1790 |
| _version_ | 1797492694482681856 |
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| author | Yitong Guo Jie Mei Zhiting Pan Haonan Liu Weiwei Li |
| author_facet | Yitong Guo Jie Mei Zhiting Pan Haonan Liu Weiwei Li |
| author_sort | Yitong Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:07:22Z |
| format | Article |
| id | doaj.art-62cf65a73f08435f841e53ba9e4e3773 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:07:22Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-62cf65a73f08435f841e53ba9e4e37732023-11-23T14:24:33ZengMDPI AGMathematics2227-73902022-05-011011179010.3390/math10111790Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk EvaluationYitong Guo0Jie Mei1Zhiting Pan2Haonan Liu3Weiwei Li4School of Business Administration, Northeastern University, Shenyang 110819, ChinaSchool of Business Administration, Northeastern University, Shenyang 110819, ChinaSchool of Business Administration, Northeastern University, Shenyang 110819, ChinaSchool of Business Administration, Northeastern University, Shenyang 110819, ChinaSchool of Business Administration, Northeastern University, Shenyang 110819, ChinaEnsemble 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.https://www.mdpi.com/2227-7390/10/11/1790credit-risk evaluationensemble learningimbalanced classificationdiversity promotionself-adaptive optimizationfuzzy sampling method |
| spellingShingle | Yitong Guo Jie Mei Zhiting Pan Haonan Liu Weiwei Li Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation Mathematics credit-risk evaluation ensemble learning imbalanced classification diversity promotion self-adaptive optimization fuzzy sampling method |
| title | Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation |
| title_full | Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation |
| title_fullStr | Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation |
| title_full_unstemmed | Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation |
| title_short | Adaptively Promoting Diversity in a Novel Ensemble Method for Imbalanced Credit-Risk Evaluation |
| title_sort | adaptively promoting diversity in a novel ensemble method for imbalanced credit risk evaluation |
| topic | credit-risk evaluation ensemble learning imbalanced classification diversity promotion self-adaptive optimization fuzzy sampling method |
| url | https://www.mdpi.com/2227-7390/10/11/1790 |
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