Combination of biorthogonal wavelet hybrid kernel OCSVM with feature weighted approach based on EVA and GRA in financial distress prediction
type: Item
instance of: scholarly article
title: Combination of biorthogonal wavelet hybrid kernel OCSVM with feature weighted approach based on EVA and GRA in financial distress prediction
zbMATH Open document ID: 1407.91270
DOI: 10.1155/2014/538594
author: Chao Huang
author: Fei Gao
author: Hong-Yan Jiang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Financial distress prediction plays an important role in the survival of companies. In this paper, a novel biorthogonal wavelet hybrid kernel function is constructed by combining linear kernel function with biorthogonal wavelet kernel function. Besides, a new feature weighted approach is presented based on economic value added (EVA) and grey relational analysis (GRA). Considering the imbalance between financially distressed companies and normal ones, the feature weighted one-class support vector machine based on biorthogonal wavelet hybrid kernel (BWH-FWOCSVM) is further put forward for financial distress prediction. The empirical study with real data from the listed companies on Growth Enterprise Market (GEM) in China shows that the proposed approach has good performance.
Mathematics Subject Classification ID: 91G60
Mathematics Subject Classification ID: 91G70
Mathematics Subject Classification ID: 91B84
zbMATH DE Number: 7016690
Wikidata QID: Q59068347
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/538594
OpenAlex ID: W2050102265
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description: scientific article; zbMATH DE number 7016690