Using Individual Feature Evaluation to Start Feature Subset Selection Methods for Classification

Using a mechanism that can select the best features in a specific data set improves precision, efficiency and the adaptation capacity in a learning process and thus the resulting model as well. Normally, data sets contain more information than what is needed to generate a certain model. Due to this, many feature selection methods have been developed. Different evaluation functions and measures are applied and a selection of the best features is generated. This contribution proposes the use of individual feature evaluation methods as starting method for search based feature subset selection methods. An in-depth empirical study is carried out comparing traditional feature selection methods with the new started feature selection methods. The results show that the proposal is interesting as time gets reduced and classification accuracy gets improved.