Feature subset selection in a methodology for training and improving artificial neural network weights and connections

Abstract

This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of multi-layer perceptron neural networks. We compare the performance of the proposed method for feature subset selection to five classical feature selection methods in three different classification problems.