A feature selection method based on Choquet Integral and Typicality Analysis
Résumé
In this paper, we present an iterative feature selection method based on feature typicality and interactivity analysis. The aim of such a method 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. In the presented application, we apply this method to wood defect classification. 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 method aims to replace expert selection by automatically choosing the most adapted features to the recognition problem.
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