Feature selection using tabu search with long-term memories and probabilistic neural networks

Feature selection is a dimensionality reduction problem in order to reduce measurement costs, shorten computational time, relieve the curse of dimensionality. and improve classification accuracy. In this paper, a hybrid approach using tabu search and probabilistic neural networks is proposed and applied to feature selection problems. The proposed tabu search algorithm differs from previous research by using a long-term memory instead of a short-term memory to avoid the necessity of the delicate tuning of the memory length and to decrease the risk of generating a cycle that traps the search in local optimal Solutions. The probabilistic neural networks integrated in the proposed hybrid approach are an outgrowth of Bayesian classifiers that outperform backpropagation-based neural networks in their global convergence and rapid training. Extensive experiments on real-world data sets are performed and the comparison with previous research indicates that the proposed hybrid approach can select an equal or smaller number of features while improving classification accuracy. (C) 2009 Elsevier B.V. All rights reserved.