Hyperparameters tuning of random forest with harmony search in credit scoring
Correct identification of defaulters and non-defaulters in the lending industry is a crucial task for financial institutions. Credit scoring is a tool utilized for credit granting decisions. Recently, Random Forest (RF) is actively researched in credit scoring due to two main benefits, i.e. non-para...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy of Sciences Malaysia
2019
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| Online Access: | http://psasir.upm.edu.my/id/eprint/80123/1/Hyperparameters%20tuning%20of%20random%20forest%20with%20harmony%20search%20in%20credit%20scoring.pdf |
| _version_ | 1796980729024872448 |
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| author | Goh, Rui Ying Lee, Lai Soon Adam, Mohd. Bakri |
| author_facet | Goh, Rui Ying Lee, Lai Soon Adam, Mohd. Bakri |
| author_sort | Goh, Rui Ying |
| collection | UPM |
| description | Correct identification of defaulters and non-defaulters in the lending industry is a crucial task for financial institutions. Credit scoring is a tool utilized for credit granting decisions. Recently, Random Forest (RF) is actively researched in credit scoring due to two main benefits, i.e. non-parametric flexibility to account for various data patterns with good classification ability and the computed features importance that can explain the attributes. Hyperparameters tuning is a necessary procedure to ensure good performance of a RF. This paper proposes the use of a metaheuristic, Harmony Search (HS), to form a hybrid HS-RF to conduct hyperparameters tuning. A Modified HS (MHS) is also proposed, forming MHS-RF, for effective yet efficient search of the RF hyperparameters. Along with parallel computing, MHS-RF effectively reduces the computational efforts of the hyperparameters tuning procedure. The proposed hybrid models are benchmarked with standard statistical models on the Lending Club peer-to-peer lending dataset. The computational results show that a well-tuned RF have better performance than statistical models, with MHS-RF reported the best performance yet being the most efficient in hyperparameters tuning of RF. |
| first_indexed | 2024-03-06T10:27:20Z |
| format | Article |
| id | upm.eprints-80123 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:27:20Z |
| publishDate | 2019 |
| publisher | Academy of Sciences Malaysia |
| record_format | dspace |
| spelling | upm.eprints-801232020-09-22T06:31:47Z http://psasir.upm.edu.my/id/eprint/80123/ Hyperparameters tuning of random forest with harmony search in credit scoring Goh, Rui Ying Lee, Lai Soon Adam, Mohd. Bakri Correct identification of defaulters and non-defaulters in the lending industry is a crucial task for financial institutions. Credit scoring is a tool utilized for credit granting decisions. Recently, Random Forest (RF) is actively researched in credit scoring due to two main benefits, i.e. non-parametric flexibility to account for various data patterns with good classification ability and the computed features importance that can explain the attributes. Hyperparameters tuning is a necessary procedure to ensure good performance of a RF. This paper proposes the use of a metaheuristic, Harmony Search (HS), to form a hybrid HS-RF to conduct hyperparameters tuning. A Modified HS (MHS) is also proposed, forming MHS-RF, for effective yet efficient search of the RF hyperparameters. Along with parallel computing, MHS-RF effectively reduces the computational efforts of the hyperparameters tuning procedure. The proposed hybrid models are benchmarked with standard statistical models on the Lending Club peer-to-peer lending dataset. The computational results show that a well-tuned RF have better performance than statistical models, with MHS-RF reported the best performance yet being the most efficient in hyperparameters tuning of RF. Academy of Sciences Malaysia 2019 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/80123/1/Hyperparameters%20tuning%20of%20random%20forest%20with%20harmony%20search%20in%20credit%20scoring.pdf Goh, Rui Ying and Lee, Lai Soon and Adam, Mohd. Bakri (2019) Hyperparameters tuning of random forest with harmony search in credit scoring. ASM Science Journal, 12 (spec.5). pp. 1-9. ISSN 1823-6782 https://www.akademisains.gov.my/asmsj/article/hyperparameters-tuning-of-random-forest-with-harmony-search-in-credit-scoring/ |
| spellingShingle | Goh, Rui Ying Lee, Lai Soon Adam, Mohd. Bakri Hyperparameters tuning of random forest with harmony search in credit scoring |
| title | Hyperparameters tuning of random forest with harmony search in credit scoring |
| title_full | Hyperparameters tuning of random forest with harmony search in credit scoring |
| title_fullStr | Hyperparameters tuning of random forest with harmony search in credit scoring |
| title_full_unstemmed | Hyperparameters tuning of random forest with harmony search in credit scoring |
| title_short | Hyperparameters tuning of random forest with harmony search in credit scoring |
| title_sort | hyperparameters tuning of random forest with harmony search in credit scoring |
| url | http://psasir.upm.edu.my/id/eprint/80123/1/Hyperparameters%20tuning%20of%20random%20forest%20with%20harmony%20search%20in%20credit%20scoring.pdf |
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