This paper proposes a novel hybrid algorithm for feature selection. This algorithm combines a global optimization algorithm called the simulated annealing algorithm based nested partitions (NP/SA). 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. We also present a detailed application of the new algorithm to a customer feature selection problem in customer recognition of a life insurance company and it is found to have great computation efficiency and convergence speed. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Yan, L., & Changrui, Y. (2007). A new hybrid algorithm for feature selection and its application to customer recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4616 LNCS, pp. 102–111). Springer Verlag. https://doi.org/10.1007/978-3-540-73556-4_13
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