FS-Studio: An extensive and efficient feature selection experimentation tool for Weka Explorer

Abstract

A new tool with a friendly graphical user interface specifically designed to perform feature selection experiments in Weka Explorer allowing parallel computation is proposed in this work. The proposed tool performs Bayesian statistical tests among the selected feature selection techniques to check whether the differences are statistically significant or not. Moreover, the recently published general-purpose metaheuristic named Coronavirus Optimization Algorithm is also adapted for feature selection and integrated in the proposed tool to search for attribute subsets, allowing its use along with any Weka attribute subset evaluation algorithm. After the feature selection process is performed, both classification and regression techniques can be applied to the dataset built with the most relevant features. Finally, the output of the whole process is sent to an exportable table, customizable by means of a bar plot, in order to gather both predicted and actual values as well as the evaluation metrics.

Keywords

• Feature selection
• Weka
• Parallel computation
• Bayesian statistical tests