Selecting relevant features for classifier optimization

Feature selection is an important data pre-processing step that comes before applying a machine learning algorithm. It removes irrelevant and redundant attributes from the dataset with an aim of improving the algorithm performance. There exist feature selection methods which focus on discovering features that are most suitable. These methods include wrappers, a subroutine of the learning algorithm itself, and filters, which discover features according to heuristics, based on the data characteristics and not tied to a specific algorithm. This paper improves the filter approach by enabling it to select strongly relevant and weakly relevant features and gives room to the researcher to decide which of the weakly relevant features to include. This new approach brings clarity and understandability to the feature selection preprocessing step