Revised HLMS: A useful algorithm for fuzzy measure identification

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.

► HLMS is a gradient descent algorithm for identifying Choquet integral coefficients.
► Two modifications are proposed to ensure convergence: update formula, monotonicity check.
► The revised version is deeply studied according to convergence, accuracy, robustness.
► Its properties make HLMS a powerful algorithm for fuzzy measure identification.