New parallel randomized algorithms for the traveling salesman problem
Computers and Operations Research - Special issue on the traveling salesman problem
A new hybrid optimization algorithm
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Feature selection in unsupervised learning via evolutionary search
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Nested Partitions Method for Global Optimization
Operations Research
An Optimization Framework for Product Design
Management Science
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent Partitioning for Feature Selection
INFORMS Journal on Computing
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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.