A feature selection method based on Choquet Integral and Typicality Analysis

An iterative feature selection method based on feature typicality and interactivity analysis is presented in this paper. The aim is to enhance model interpretability by selecting the best significant features among a list extracted from images. The inference mechanism uses a fuzzy linguistic rule-based system. This method is applied here to a wood defect classification problem. Nowadays, feature selection is expertise-driven and most of the time, expert uses features by habits which not always represent the best ones to use. The proposed approach aims to replace expert selection by automatically choosing a suitable set of features to the recognition problem.