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An Efficient Algorithm for Sampling Fuzzy Measures
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AI
DOI：10.1109/TFUZZ.2024.3384954.png)
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Abstract
En 中文
Random generation of fuzzy measures can be viewed as assigning 2(n )ordered random values from the unit interval to the linear extension of power-sets of inputs. Several recently proposed methods for constructing linear extensions have high computational cost. We propose a numerically efficient approach that directly obtains the linear extensions from convex combinations of 0-1 fuzzy measures, reducing the computational complexity to O(n2(n)). The resulting algorithm is very short but is effective in generating a broad range of fuzzy measures as demonstrated by experiments. The full C++ code is presented.
Keywords:
0-1 games
capacities
fuzzy measures
linear extension
random generation
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