کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
528268 | 869545 | 2013 | 9 صفحه PDF | دانلود رایگان |
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.
Journal: Information Fusion - Volume 14, Issue 4, October 2013, Pages 532–540