Evaluation of Feature Extraction Methods on Software Cost Estimation

Abstract

This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use principal component analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods with support vector regression (SVR) on publicly available datasets. Our results for these datasets indicate there is no significant difference between the performances of these linear and non-linear feature extraction methods.