An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization

— 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.

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Paulo Vinicius Wolski Radtke, Tony Wong, Robert Sabourin
• Keywords: Classifier Optimization Problems, IJCNN 2006, Optimization Process, Solution Over-fit Problem