Applied Soft Computing Journal, volume 84, pages 105740

Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring

Pławiak Paweł ¹

Abdar Moloud ²

Rajendra Acharya U. ³

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Institute of Telecomputing, Faculty of Physics, Mathematics and Computer Science Cracow University of Technology, Warszawska 24 st., F-5, 31-155 Krakow, Poland |

Department of Computer Science, University of Quebec in Montreal, Montreal (QC), H2X 3Y7, Canada |

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore |

Publication type: Journal Article

Publication date: 2019-11-01

Elsevier

Journal: Applied Soft Computing Journal

Quartile SCImago

Quartile WOS

Impact factor: 8.7

ISSN: 15684946

DOI: 10.1016/j.asoc.2019.105740

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Abstract

In the recent decades, credit scoring has become a very important analytical resource for researchers and financial institutions around the world. It helps to boost both profitability and risk control since bank credits plays a significant role in the banking industry. In this study, a novel approach based on deep genetic cascade ensemble of different support vector machine (SVM) classifiers (called Deep Genetic Cascade Ensembles of Classifiers (DGCEC)) is applied to the Statlog Australian data. The proposed approach is a hybrid model which merges the benefits of: (a) evolutionary computation, (b) ensemble learning, and (c) deep learning. The proposed approach comprises of a novel 16-layer genetic cascade ensemble of classifiers, having: two types of SVM classifiers, normalization techniques, feature extraction methods, three types of kernel functions, parameter optimizations, and stratified 10-fold cross-validation method. The general architecture of the proposed approach consists of ensemble learning, deep learning, layered learning, supervised training, feature (attributes) selection using genetic algorithm, optimization of parameters for all classifiers by using genetic algorithm, and a new genetic layered training technique (for selection of classifiers). Our developed model achieved the highest prediction accuracy of 97.39%. Hence, our proposed approach can be employed in the banking system to evaluate the bank credits of the applicants and aid the bank managers in making correct decisions. • A novel deep genetic cascade ensemble of SVM classifiers, DGCEC technique is proposed. • The DGCEC is used to predict statlog Australian credit approval to credit scoring. • A 16-layer cascade-based structure of DGCEC comprises of SVM classifiers. • A genetic layered training based on mimicking mechanism of tutoring is applied. • Obtained classification accuracy of 97.39% (18 errors/690).

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Soft Computing	Soft Computing, 1, 1.01% Soft Computing 1 publication, 1.01%
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Citations by publishers

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Elsevier	Elsevier, 38, 38.38% Elsevier 38 publications, 38.38%
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Hindawi Limited	Hindawi Limited, 2, 2.02% Hindawi Limited 2 publications, 2.02%
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SAGE	SAGE, 1, 1.01% SAGE 1 publication, 1.01%
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EDP Sciences	EDP Sciences, 1, 1.01% EDP Sciences 1 publication, 1.01%
IEEE	IEEE, 1, 1.01% IEEE 1 publication, 1.01%
American Institute of Physics (AIP)	American Institute of Physics (AIP), 1, 1.01% American Institute of Physics (AIP) 1 publication, 1.01%
CAIRN	CAIRN, 1, 1.01% CAIRN 1 publication, 1.01%
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Pławiak P., Abdar M., Rajendra Acharya U. Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring // Applied Soft Computing Journal. 2019. Vol. 84. p. 105740.

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TY - JOUR

DO - 10.1016/j.asoc.2019.105740

UR - https://doi.org/10.1016%2Fj.asoc.2019.105740

TI - Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring

T2 - Applied Soft Computing Journal

AU - Pławiak, Paweł

AU - Abdar, Moloud

AU - Rajendra Acharya, U.

PY - 2019

DA - 2019/11/01 00:00:00

PB - Elsevier

SP - 105740

VL - 84

SN - 1568-4946

ER -

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@article{2019_Pławiak,

author = {Paweł Pławiak and Moloud Abdar and U. Rajendra Acharya},

title = {Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring},

journal = {Applied Soft Computing Journal},

year = {2019},

volume = {84},

publisher = {Elsevier},

month = {nov},

url = {https://doi.org/10.1016%2Fj.asoc.2019.105740},

pages = {105740},

doi = {10.1016/j.asoc.2019.105740}

}

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Publisher

Elsevier

Journal

Applied Soft Computing Journal

Quartile SCImago

Quartile WOS

Impact factor

8.7

ISSN

15684946 (Print)