Journal
INFORMATION FUSION
Volume 14, Issue 4, Pages 532-540Publisher
ELSEVIER
DOI: 10.1016/j.inffus.2013.01.002
Keywords
Choquet integral; Gradient descent; Convergence; Multicriteria
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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. (c) 2013 Elsevier B.V. All rights reserved.
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