Evidential Supervised Classifier System: A New Learning Classifier System Dealing with Imperfect Information

Rahma Ferjani, Lilia Rejeb, Chedi Abdelkarim and Lamjed Ben Said
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Rahma Ferjani: SMART Lab, Institut Supérieur de Gestion de Tunis (ISG Tunis), Université de Tunis, Rue de la liberté, Bardo 2000, Tunisia
Lilia Rejeb: SMART Lab, Institut Supérieur de Gestion de Tunis (ISG Tunis), Université de Tunis, Rue de la liberté, Bardo 2000, Tunisia
Chedi Abdelkarim: SMART Lab, Institut Supérieur de Gestion de Tunis (ISG Tunis), Université de Tunis, Rue de la liberté, Bardo 2000, Tunisia
Lamjed Ben Said: SMART Lab, Institut Supérieur de Gestion de Tunis (ISG Tunis), Université de Tunis, Rue de la liberté, Bardo 2000, Tunisia

International Journal of Information Technology & Decision Making (IJITDM), 2024, vol. 23, issue 02, 917-938

Abstract: Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems.

Keywords: Imprecision; learning classifier systems; belief function theory; supervised learning; imperfect information (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219622022500997

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