Title: An ant colony optimisation and Nelder-Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks
Authors: Nigib Sharma; Nampally Arun; Vadlamani Ravi
Addresses: Department of Mathematics, Indian Institute of Technology, Kharagpur, 721302, West Bengal, India ' Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500057, Andhra Pradesh, India ' Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500057, Andhra Pradesh, India
Abstract: In this paper, we propose the application of a hybrid meta-heuristic, viz., ant colony optimisation-Nelder-Mead simplex hybrid (ACONM) for training multi-layer perceptron MLP instead of the back-propagation algorithm to solve classification problems. The resulting network is called ACONM-NN. In the ACONM, the ACO part is employed to find a promising region in the search space and then the simplex search is applied to quickly converge to the optimum solution. The influence of the parameters on the performance of the algorithm is also investigated. The effectiveness of the ACONM-NN is tested on the three benchmark datasets viz., Iris, Wine and Wisconsin Breast cancer and bankruptcy prediction problem in banks. The bankruptcy datasets analysed are that of Spanish, Turkish and US banks. Throughout the study we performed ten-fold cross validation. Based on the experiments, it is clear that the proposed ACONM-NN outperformed many other approaches in terms of accuracy.
Keywords: ant colony optimisation; ACO; Nelder-Mead simplex method; multi-layer perceptron; bankruptcy prediction; banks; neural network training; neural networks; Spain; Turkey; USA; United States; banking industry.
International Journal of Information and Decision Sciences, 2013 Vol.5 No.2, pp.188 - 203
Published online: 28 Feb 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article