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Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network
Big Data ( IF 2.6 ) Pub Date : 2021-04-16 , DOI: 10.1089/big.2020.0158
Noura Metawa 1, 2 , Irina V Pustokhina 3 , Denis A Pustokhin 4 , K Shankar 5 , Mohamed Elhoseny 6, 7
Affiliation  

At present times, financial decisions are mainly based on the classifier technique, which is utilized to allocate a collection of observations into fixed groups. A diverse set of data classifier approaches were presented for forecasting the financial crisis of an institution using the past data. An essential process toward the design of a precise financial crisis prediction (FCP) approach comprises the choice of proper variables (features) that are related to the issues at hand. This is termed as a feature selection (FS) issue that assists to improvise the classifier results. Besides, computational intelligence techniques can be used as a classification model to determine the financial crisis of an organization. In this view, this article introduces a new FS using elephant herd optimization (EHO) with modified water wave optimization (MWWO) algorithm-based deep belief network (DBN) for FCP. The EHO algorithm is applied as a feature selector, and MWWO-DBN is utilized for the classification process. The application of the MWWO algorithm helps to tune the parameters of the DBN model, and the choice of optimal feature subset from the EHO algorithm leads to enhanced classification performance. The experimental results of the proposed model are tested against three benchmark data sets, namely AnalcatData, German Credit, and Australian Credit. The obtained simulation results indicated the superior performance of the proposed model by attaining maximum classification performance.

中文翻译:

基于计算智能的金融危机预测模型使用具有最优深度信念网络的特征子集选择

目前,财务决策主要基于分类器技术,该技术用于将一组观察结果分配到固定组中。提出了一组不同的数据分类方法,用于使用过去的数据预测机构的金融危机。设计精确的金融危机预测 (FCP) 方法的一个基本过程包括选择与手头问题相关的适当变量(特征)。这被称为特征选择 (FS) 问题,有助于改进分类器结果。此外,计算智能技术可以用作分类模型来确定组织的财务危机。在这种观点下,本文介绍了一种使用大象群优化 (EHO) 和基于改进水波优化 (MWWO) 算法的 FCP 深度信念网络 (DBN) 的新 FS。EHO 算法用作特征选择器,MWWO-DBN 用于分类过程。MWWO 算法的应用有助于调整 DBN 模型的参数,并且从 EHO 算法中选择最优特征子集可以提高分类性能。所提出模型的实验结果针对三个基准数据集进行了测试,即 AnalcatData、德国信贷和澳大利亚信贷。获得的仿真结果表明,通过获得最大的分类性能,所提出的模型具有优越的性能。MWWO-DBN 用于分类过程。MWWO 算法的应用有助于调整 DBN 模型的参数,并且从 EHO 算法中选择最优特征子集可以提高分类性能。所提出模型的实验结果针对三个基准数据集进行了测试,即 AnalcatData、German Credit 和 Australia Credit。获得的仿真结果表明,通过获得最大的分类性能,所提出的模型具有优越的性能。MWWO-DBN 用于分类过程。MWWO 算法的应用有助于调整 DBN 模型的参数,并且从 EHO 算法中选择最优特征子集可以提高分类性能。所提出模型的实验结果针对三个基准数据集进行了测试,即 AnalcatData、German Credit 和 Australia Credit。获得的仿真结果表明,通过获得最大的分类性能,所提出的模型具有优越的性能。所提出模型的实验结果针对三个基准数据集进行了测试,即 AnalcatData、德国信贷和澳大利亚信贷。获得的仿真结果表明,通过获得最大的分类性能,所提出的模型具有优越的性能。所提出模型的实验结果针对三个基准数据集进行了测试,即 AnalcatData、German Credit 和 Australia Credit。获得的仿真结果表明,通过获得最大的分类性能,所提出的模型具有优越的性能。
更新日期:2021-04-18
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