Improving the Performance of Artificial Neural Networks via Instance Selection and Feature Dimensionality Reduction

Abstract

This paper presents a hybrid approach with two phases for improving the performance of training artificial neural networks (ANNs) by selection of the most important instances for training, and then reduction the dimensionality of features. The ANNs which are applied in this paper for validation, are included Multi-Layer Perceptron (MLP) and Neuro-Fuzzy Network (NFN). In the first phase, the Modified Fast Condensed Nearest Neighbor (MFCNN) algorithm is used to construct the subset with instances very close to the decision boundary. It leads to achieve the instances more useful for training the network. And in the second phase, an Ant-based approach to the supervised reduction of feature dimensionality is introduced, aims to reduce the complexity, and improve the accuracy of learning the ANN. The main purpose of this method is to enhance the classification performance by improving the quality of the training set. Experimental results illustrated the efficiency of the proposed approach.