Please use this identifier to cite or link to this item:
Title: A new hybrid algorithm for bankruptcy prediction using switching particle swarm optimization and support vector machines
Authors: Lu, Y
Zeng, N
Liu, X
Yi, S
Issue Date: 2015
Publisher: Hindawi Publishing Corporation
Citation: Discrete Dynamics in Nature and Society, 2015:294930, (2015)
Abstract: Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algorithm can largely improve the explanatory power and the stability of the SVM. The proposed algorithm is successfully applied in the bankruptcy prediction problem, where experiment data sets are originally from the UCI Machine Learning Repository. The simulation results show the superiority of proposed algorithm over the traditional SVM-based methods combined with genetic algorithm (GA) or the particle swarm optimization (PSO) algorithm alone.
ISSN: 1026-0226
Appears in Collections:Publications

Files in This Item:
File Description SizeFormat 
Fulltext.pdf2.21 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.