A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a SOM-based classifier

Variable selection is an important task in machine learning and data mining applications. In many real world problems a huge volume of data is often available which corresponds to a large number of variables. When developing a model for classification, clustering or other applications, the search of an optimal subset of relevant input variables is crucial. In this paper an automatic variable selection method, which combines Genetic Algorithms and Self Organizing Maps, is proposed for classification purposes. The Genetic Algorithm is used to select the most relevant input variables and to set some relevant parameters of a classifier implemented through a Self Organizing Map. This method has been tested with several datasets belonging to the UCI repository. The results of the tests are presented and discussed in this paper. The proposed approach provides a good classification accuracy and contributes to the comprehension of the phenomenon under consideration.