Revised HLMS: a useful algorithm for fuzzy measure identification

J. Murillo (1) , Serge Guillaume (2) , E. Tapia (1) , P. Bulacio (1)

1 CONICET - Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires]
2 UMR ITAP - Information – Technologies – Analyse Environnementale – Procédés Agricoles

An important limitation of fuzzy integrals for information fusion is the exponential growth of coefficients for an increasing number of information sources. To overcome this problem a variety of fuzzy measure identification algorithms has been proposed. HLMS is a simple gradient-based algorithm for fuzzy measure identification which suffers from some convergence problems. In this paper, two proposals for HLMS convergence improvement are presented, a modified formula for coefficients update and new policy for monotonicity check. A comprehensive experimental work shows that these proposals indeed contribute to HLMS convergence, accuracy and robustness.

Mots clés: ALGORITHME; MESURE; CONVERGENCE; CHOQUET INTEGRAL; GRADIENT DESCENT; MULTICRITERIA

Domaines: Sciences de l'environnement

J. Murillo, Serge Guillaume, E. Tapia, P. Bulacio. Revised HLMS: a useful algorithm for fuzzy measure identification. Information Fusion, 2013, 14 (4), p. 532 - p. 540. ⟨10.1016/j.inffus.2013.01.002⟩. ⟨hal-00862610⟩