Fast Dual Selection using Genetic Algorithms for Large Data Sets

Abstract

This paper is devoted to feature and instance selection managed by genetic algorithms (GA) in the context of supervised classification. We propose a GA encoded by binary chromosomes having the same size as the feature space for selecting features in which each evaluated chromosome delivers a set of instances. The main aim is to optimize the processing time, which is particularly problematic when handling large databases. A key feature of our approach is the variable fitness evaluation based on scalability methodologies. Experimental results indicate that the preliminary version of the proposed algorithm can significantly reduce the computation time and is therefore applicable to high-dimensional data sets.