Fuzzy Rough Set Prototype Selection for Regression

Abstract

Instance selection methods are a class of preprocessing techniques that have been widely studied in machine learning to remove redundant or noisy instances from a training set. The main focus of such prior efforts has been on the selection of suitable training instances to perform a classification task for crisp class labels. In this paper, we propose a novel instance selection technique termed Fuzzy Rough Set Prototype Selection for Regression (FRPS-R) for solving regression problems, where the outcome is continuous. We use concepts from fuzzy rough set theory and extend the currently well-known fuzzy rough set prototype selection technique to model the quality of all available elements and then use a wrapper approach to select an optimal subset of high-quality instances; thereby generalizing the idea. Our experimental evaluation shows that the application of our proposed instance selection technique can significantly improve the predictive performance of the weighted k-nearest neighbor regression algorithm, in particular when noise is present in the original training set.