关键词: 数据流分类, 子分类器序列, 支持向量机, 柔性漂移

Abstract: Time adaptive support vector machine and its improved method has shown preferable performance on data stream classification by simultaneously solving several sub-classifiers locally and globally in an alternative kernel space to enforce the sub-classifiers evolve stably. However, due to the randomness of data in many practical scenarios, concept drift in steady rhythm cannot be guaranteed, thereby resulting in that such instability of sub-classifier serials should be consider in TA-SVM method. In this paper, in order to loosen the constrains on normal vector and bias and margin of sub-classifiers, a novel classifier named Flexible Drift Support Vector Machines（FD-SVM） is proposed by handling normal vector and bias discriminatory when sub-classifier drifting. The effectiveness of the proposed FD-SVM is experimentally verified.

Key words: data stream classification, sub-classifiers serials, support vector machine, flexible drift