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Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring
Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framew...
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| Published in: | PloS one 2020-06, Vol.15 (6), p.e0234254-e0234254 |
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| creator | Zhang, Runchi Qiu, Zhiyi |
| description | Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases. |
| doi_str_mv | 10.1371/journal.pone.0234254 |
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However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0234254</identifier><identifier>PMID: 32502197</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Artificial intelligence ; Artificial neural networks ; Back propagation networks ; Biology and Life Sciences ; Computer and Information Sciences ; Credit ratings ; Decision making ; Discriminant analysis ; Engineering and Technology ; Intelligence ; Mathematical models ; Model accuracy ; Neural networks ; Novels ; Optimization ; Parameters ; Physical Sciences ; Research and Analysis Methods ; Scoring ; Scoring models ; Social Sciences ; Swarm intelligence ; Time ; Training</subject><ispartof>PloS one, 2020-06, Vol.15 (6), p.e0234254-e0234254</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Zhang, Qiu. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Zhang, Qiu 2020 Zhang, Qiu</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c562t-bb33508f94a47c594ec41451ebf8185cf19a7e5008ca28d97a0a659a0fe6e7673</citedby><cites>FETCH-LOGICAL-c562t-bb33508f94a47c594ec41451ebf8185cf19a7e5008ca28d97a0a659a0fe6e7673</cites><orcidid>0000-0001-9862-859X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2409870108/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2409870108?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25730,27900,27901,36988,36989,44565,53765,53767,75095</link.rule.ids></links><search><creatorcontrib>Zhang, Runchi</creatorcontrib><creatorcontrib>Qiu, Zhiyi</creatorcontrib><title>Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring</title><title>PloS one</title><description>Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Back propagation networks</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Credit ratings</subject><subject>Decision making</subject><subject>Discriminant analysis</subject><subject>Engineering and Technology</subject><subject>Intelligence</subject><subject>Mathematical models</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Novels</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Physical Sciences</subject><subject>Research and Analysis Methods</subject><subject>Scoring</subject><subject>Scoring models</subject><subject>Social Sciences</subject><subject>Swarm 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Runchi</au><au>Qiu, Zhiyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring</atitle><jtitle>PloS one</jtitle><date>2020-06-05</date><risdate>2020</risdate><volume>15</volume><issue>6</issue><spage>e0234254</spage><epage>e0234254</epage><pages>e0234254-e0234254</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32502197</pmid><doi>10.1371/journal.pone.0234254</doi><orcidid>https://orcid.org/0000-0001-9862-859X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial intelligence Artificial neural networks Back propagation networks Biology and Life Sciences Computer and Information Sciences Credit ratings Decision making Discriminant analysis Engineering and Technology Intelligence Mathematical models Model accuracy Neural networks Novels Optimization Parameters Physical Sciences Research and Analysis Methods Scoring Scoring models Social Sciences Swarm intelligence Time Training |
| title | Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring |
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