关键词: 粗糙集, 支持向量机, 分解算法, 属性约简, 边界集, 时空性能

Abstract:

When training the high-dimension and large-sample objectives,the support vector machine（SVM） 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 machine（RS-SMO） by combining the sequence minimizing optimization（SMO） 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 SMO algorithm.Thus,the dimension and scale of the training set become less than both of the original sets,which helps to improve the time-space performance of the algorithm.Experimental results indicate that the proposed RS-SMO algorithm minimizes the structural risk and is superior to the SMO algorithm in its performance.

Key words: rough set, support vector machine, decomposing algorithm, attribute reduction, boundary set, time-space performance