Semi-overlap Function Based on Fuzzy Rough Set and its Application in Image Processing and Feature Selection
In this paper,we innovatively propose the(I,Os)-fuzzy rough set model by combining the semi-overlap function and fuzzy rough set.Theoretically,we utilize the semi-overlap function with looser constraints to construct the upper approximation operator of the model,which makes the model have higher flexibility and adaptability.In practice,we combine the model with the fuzzy C-mean algorithm to propose a new image edge extraction algorithm.The algorithm is able to accurately extract complete image edges at a lower noise introduction rate.Meanwhile,we also propose a new feature selection algorithm based on the(I,Os)-fuzzy rough set model,which is able to select fewer attribute conditions compared to the traditional algorithm while maintaining high classification accuracy.Experiments prove that our proposed model and algorithm are feasible and effective,and provide strong support for research and application in related fields.
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