Abstract:

In this paper, we propose a new classification method that improves the support vector machines technique (SVM). It consists of the real time SVM (RTSVM) that uses an incremental version of SVM which is the LASVM. It also takes into account of new data over time. Actually, current classification techniques suffer from scalability problem. There is a permanent growing and evolution of data. Besides, there are a need of important memory capacity and execution time to deal with data stream. Although the improvement made to SVM to reduce the memory use and computational time in training phase, the obtained model in training phase cannot be applied to new observations in test phase without using the hole data. To overcome this issue and improve classification task in test phase, the RTSVM adapts the initial model produced by the LASVM. After that, the RTSVM updates and improves it in test phase by only using new data for re-training. As a result, our proposal considerably reduces the execution time and improves the accuracy especially in test phase. Empirical study shows RTSVM to be effective when using real-world datasets.