Genetic algorithms for exploratory data analysis

Abstract

Data projection is a commonly used technique applied to analyse high dimensional data. In the present work, we propose a new data projection method that uses genetic algorithms to find linear projections, providing meaningful representations of the original data. The proposed technique is compared with well known methods as Principal Components Analysis (PCA) and neural networks for non-linear discriminant analysis (NDA). A comparative study of these methods with several data sets is presented.