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Revised HLMS: A useful algorithm for fuzzy measure identification
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DOI：10.1016/j.inffus.2013.01.002.png)
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摘要
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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.
Keyword:
Choquet integral
Gradient descent
Convergence
Multicriteria
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