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首页> 外文期刊>International journal of machine learning and cybernetics >A novel classification algorithm based on kernelized fuzzy rough sets
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A novel classification algorithm based on kernelized fuzzy rough sets

机译：一种基于封闭模糊粗糙集的新型分类算法

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摘要

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.
机译：模糊内核是一种特殊类型的内核，通常用于计算上部和较低近似，以及封闭模糊粗糙集中的正区域，并且正区域表征了条件属性和决策属性之间的一致性程度。当分类超平面存在于两类样本之间时，正区域被转换为与样本到分类过平面的距离之和。正区域越大，一致性越高。在本文中，我们构建了一种新型模型，可以解决近核模糊粗糙集中正区的几何含义的分类超平面。然后，通过将来自样本的距离的总和最大化到分类超平面来开发分类模型，并且这种解决该目标函数的优化问题被转换为其双问题。实验结果表明，该分类算法是有效的。

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