Motivation: Datasets with high dimensionality represent a challenge to existing learning methods. The presence of irrelevant and redundant features in a dataset can degrade the performance of the models inferred from it. In large datasets, manual management of features tends to be impractical. Therefore, the development of automatic discovery techniques to remove useless features has attracted increasing interest. In this paper, we propose a novell framework to select relevant features in supervised datasets.
Availability: This tool can be downloaded from https://github.com/ivangarcia88/selection
Results: This tool allow to identify relevant and remove redundant features, reducing computation time on training a machine learning model while improving the performance.