Learning under concept drift with support vector machines

Abstract

Support Vector Machines (SVMs) have been recognized as one of the most successful classification methods for many applications in static environment. However in dynamic environment, data characteristics may evolve over time. This leads to deteriorate dramatically the performance of SVMs over time. This is because of the use of data which is no more consistent with the characteristics of new incoming one. Thus in this paper, we propose an approach to recognize and handle concept changes with support vector machine. This approach integrates a mechanism to use only the recent and most representative patterns to update the SVMs without a catastrophic forgetting. © 2014 Springer International Publishing Switzerland.