Use of circle-segments as a data visualization technique for feature selection in pattern classification

Abstract

One of the issues associated with pattern classification using data-based machine learning systems is the "curse of dimensionality". In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour(kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle-segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets. © 2008 Springer-Verlag Berlin Heidelberg.