Abstract
Fuzzy kernels are a special kind of kernels which are usually employed to calculate the upper and lower approximations, as well as the positive region in kernelized fuzzy rough sets, and the positive region characterizes the degree of consistency between conditional attributes and decision attributes. When the classification hyperplane exists between two classes of samples, the positive region is transformed into the sum of the distances from the samples to classification hyperplane. The larger the positive region, the higher the degree of consistency. In this paper, we construct a novel model to solve the classification hyperplane from the geometric meaning of the positive region in kernelized fuzzy rough sets. Then, a classification model is developed through maximizing the sum of the distances from the samples to classification hyperplane, and this optimization problem that addresses this objective function is transformed to its dual problem. Experimental results show that the proposed classification algorithm is effective.
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