Authors: Luo Yan, Yu Changrui
Addresses: The Sydney Institute of Language and Commerce, Shanghai University, 20 Chengzhong RD, Shanghai 201800, China. ' School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding RD, Shanghai 200433, China
Abstract: An important problem in customer recognition process is to select the most valid customer features. The authors present a new optimisation algorithm that combines a global optimisation algorithm called the nested partitions algorithm and the simulated annealing method. The resulting hybrid algorithm NP/SA retains the global perspective of the nested partitions algorithm and the local search capabilities of the simulated annealing method. A detailed application of the new algorithm to a customer recognition problem is also presented. The numerical results suggest that the new framework has great computation efficiency and convergence speed and is very efficient for a difficult customer feature selection problem.
Keywords: feature selection; customer recognition; optimisation algorithms; nested partitions; customer features; nested partitions; simulated annealing.
International Journal of Services Operations and Informatics, 2009 Vol.4 No.2, pp.146 - 158
Available online: 23 Feb 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article