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Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Kavitha Muthukumaran*, K. Hariharanath, Vani Haridasan
SSN School of Management, Kalavakkam, Chennai, 603110, India
* Corresponding Author: Kavitha Muthukumaran. Email:

Computer Systems Science and Engineering 2023, 45(1), 887-901. https://doi.org/10.32604/csse.2023.030627

Received 29 March 2022; Accepted 17 May 2022; Issue published 16 August 2022

Abstract

Financial crisis prediction (FCP) received significant attention in the financial sector for decision-making. Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy. Conventionally, numerous approaches have been developed in the design of accurate FCP processes. At the same time, classifier efficacy and predictive accuracy are inadequate for real-time applications. In addition, several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets. Thus, there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets. This paper presents a novel multi-vs. optimization (MVO) based feature selection (FS) with an optimal variational auto encoder (OVAE) model for FCP. The proposed multi-vs. optimization based feature selection with optimal variational auto encoder (MVOFS-OVAE) model mainly aims to accomplish forecasting the financial crisis. For achieving this, the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization. In addition, the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach. Followed by, the variational auto encoder (VAE) model is applied for the categorization of financial data into financial crisis or non-financial crisis. Finally, the differential evolution (DE) algorithm is utilized for the parameter tuning of the VAE model. A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Keywords

Financial crisis prediction; forecasting; feature selection; data classification; machine learning

Cite This Article

K. Muthukumaran, K. Hariharanath and V. Haridasan, "Feature selection with optimal variational auto encoder for financial crisis prediction," Computer Systems Science and Engineering, vol. 45, no.1, pp. 887–901, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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