Classification algorithm based on rough set and support vector data description
Peng Min, Qiang Li, Jianghong · 2011
When training the high-dimension and large-sample objectives, the support vector data description (SVDD) may encounter the curse of dimensionality and may result in large time cost. In order to solve these problems, this paper presents a novel classification algorithm based on rough set and support vector data description (RS-SVDD) by combining the support vector machine (SVM) algorithm with the data processing function of a rough set. In this algorithm, data sets are attribute reduced according to the attribute significance, and some class boundary sets are formed by using rough boundary set as the training subsets of SVDD algorithm. Thus, the dimension and scale of the training set become less than both of the original sets, which helps to improve the performance of the algorithm. Experimental results indicate that the proposed RS-SVDD algorithm minimizes the structural risk and is superior to the SVDD algorithm in its performance.