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Discrete Dynamics in Nature and Society
Volume 2015, Article ID 294930, 7 pages
Research Article

A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines

1College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, China
2Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 361005, China
3Department of Computer Science, Brunel University, Uxbridge, Middlesex UB8 3PH, UK
4Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Received 27 November 2014; Accepted 19 December 2014

Academic Editor: Zidong Wang

Copyright © 2015 Yang Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.