Further Research on Feature Selection and Classification Using Genetic Algorithms

Abstract

. This paper summarizes work on an approach that combines feature selection and data classification using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classification by searching for an optimal feature weighting, essentially warping the feature space to coalesce individuals within groups and to separate groups from one another. This approach has proven especially useful with large data sets where standard feature selection techniques are computationally expensive. Second, it describes our implementation of the approach in a parallel processing environment, giving nearly linear speed-up in processing time. Third, it will summarize our present results in using the technique to discover the relative importance of features in large biological test sets. Finally, it will indicate areas for future research. 1 The Problem We live in the age of information where data is plentiful, to the extent that we are typic...