关键词: 特征缺省;类内方差;支持向量机;模式分类

Abstract:

In the classification of data with absent features, the lately proposed support vector machine with absent features (AFSVM) has some drawbacks: the obtained classification hyper plane with AF-SVM can not well adapt to the data’s overall distribution, and the proportion of the misclassified data differs greatly between the two classes. To overcome these drawbacks, a minimum within-class variance SVM with absent features (AF-V-SVM) was proposed based on the technology of minimum class variance SVM (MCVSVM).On the one hand, AF-V-SVM could improve the direction of the classification hyper plane with the information of the distribution feature of the data set; on the other hand, this method adjusted the proportion of misclassified data by freely setting the definition space of classification margin. Experiments showed that the method in this paper was superior to other absent features based classification methods in the aspects of classification accuracy and rationality.

Key words: feature absence; within-class variance; support vector machine; pattern classification