An evaluation of over-fit control strategies for multi-objective evolutionary optimization

An evaluation of over-fit control strategies for multi-objective evolutionary optimization

Radtke, Paulo V.W. and Wong, Tony and Sabourin, Robert

IEEE International Conference on Neural Networks – Conference Proceedings 2006

Abstract : The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the training data sets instead of producing a general model. This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a Projection Distance classifier and a Multiple Layer Perceptron neural network classifier, in both single and ensemble of classifier configurations. Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process. © 2006 IEEE.