Paper Info
Title
Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction
Abstract
In present days, prediction of financial crisis of a company is a hot research area. The use of data mining and machine learning algorithms assists to resolve the financial crisis prediction (FCP) problem. Since financial data contain more demographical and unwanted information, it might decrease the classification performance significantly. So, feature selection (FS) process is applied to choose useful data and remove the irrelevant repetitive data. This paper introduces a novel predictive framework for FCP model by the incorporation of improved grey wolf optimization (IGWO) and fuzzy neural classifier (FNC). An IGWO algorithm is derived by the integration of GWO algorithm and tumbling effect. The presented IGWO-based FS method is employed to discover the optimal features from the financial data. For classification purposes, FNC is employed. The proposed method is experimented on two benchmark data sets, namely Australian Credit and German data set under several of performance metrics. The experimental values verified the superior nature of the proposed FCP model over the compared methods.
Year
DOI
Venue
2020
10.1007/s00500-019-04323-6
Soft Computing
Keywords
Field
DocType
Financial crisis prediction, Feature selection, Fuzzy neural classifier, Grey wolf optimization
Data set,Feature selection,Financial crisis,Fuzzy neural,Computer science,Artificial intelligence,Classifier (linguistics),Machine learning,Gray (horse)
Journal
Volume
Issue
ISSN
24
1
1432-7643
Citations 
PageRank 
References 
2
0.35
0
Authors
6
Name
Order
Citations
PageRank
Shweta Sankhwar122.04
Deepak Gupta222246.25
K. C. Ramya3102.20
S Sheeba Rani4101.19
K. Shankar59513.88
S. K. Lakshmanaprabu6353.41