粗支持向量机分类建模方法

为了克服样本模式的复杂性、噪声的影响以及信息的不完整性问题,利用粗糙集和支持向量机(SVM)的优点,把粗糙集理论用于二分类球形SVM,提出一种称为粗支持向量机分类建模方法.粗糙集具有刻画不确定、不完整数据和复杂模式的能力,分类结果能够体现出数据的不确定性,但是它不仅不具备良好的学习能力,而且也不能保证分类模型具有良好的推广能力;SVM具有良好的推广性能,但是对不确定数据的建模能力较差.本文把分类结果分为正域、边界域和负域,由此来判断不确定数据样本的分类结果的不确定性程度.通过调整参数来调节边界的宽度和允许建模的在野点样本的比例,提高分类模型的灵活性.仿真结果说明了算法的有效性.

A modeling method for classification of rough support vector machine

In order to overcome the effects induced by the complexity of sample pattern,noises and the non-integrality of information,a modeling method called rough SVM is put forward by applying the theory of rough set to binary hyper-sphere SVM and employing the advantages of rough set and SVM. Rough set has the capability of describing uncertain,non-integrated data and complex pattern,but it has no good learning capability and can not ensure the generalization of classification model. SVM possesses the outstanding performance of generalization,but it has bad capability of modeling uncertain data. Classification results in this paper are divided into positive field,boundary field and negative field,by which the uncertain degree of classification results is judged. Through adjusting the parameters,the width of boundary and the ratio of outliers allowed to the model can be adjusted,and flexibility of the classification model can be improved. Simulation results show the availability of this modeling method.