Библиографическое описание:Credit and Loan Approval Classification Using a Bio-Inspired Neural Network : научное издание / Spyridon D. Mourtas [et al.]. - Текст : непосредственный // Biomimetics. - 2024. - Т. 9, № 2. - P. 120. - ISSN 2313-7673, DOI 10.3390/biomimetics9020120.
Аннотация:<jats:p>Numerous people are applying for bank loans as a result of the banking industry’s expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions.</jats:p>
Держатель оригинала документа:Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18000 Niš, Serbia, Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Prospect Krasnoyarskiy Rabochiy 31, 660037 Krasnoyarsk, Russia, Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, Prospect Svobodny 79, 660041 Krasnoyarsk, Russia
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