Revised HLMS: A useful algorithm for fuzzy measure identification

JAVIER MURILLO; SERGE GUILLAUME; ELIZABETH TAPIA; PILAR BULACIO

INFORMATION FUSION

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2013 vol. 14 p. 532 - 540

1566-2535

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.