Abstract
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.
This research work is supported by the Natural Science Fund of China ( # 70501022) and China Postdoctoral Science Foundation ( # 20060400169).
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Yan, L., Changrui, Y. (2007). A New Hybrid Algorithm for Feature Selection and Its Application to Customer Recognition. In: Dress, A., Xu, Y., Zhu, B. (eds) Combinatorial Optimization and Applications. COCOA 2007. Lecture Notes in Computer Science, vol 4616. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73556-4_13
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DOI: https://doi.org/10.1007/978-3-540-73556-4_13
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