Incremental Real Time Support Vector Machines

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

This paper investigates the problem of handling large data stream and adding new attributes over time. We propose a new approach that employs the dynamic learning when classifying dynamic datasets. Our proposal consists of the incremental real time support vector machines (I-RTSVM) which is an improved version of the support vector machines (SVM) and LASVM. On one hand, the I-RTSVM handles large databases and uses the model produced by the LASVM to train data. It updates this model to be appropriate to new observations in test phase without re-training. On the other hand, the I-RTSVM presents a dynamic approach that adds attributes over time. It uses the final model of classification and updates it with new attributes without re-training from the beginning. Experiments are illustrated using real-world UCI databases and by applying different evaluation criteria. Results of comparison between the I-RTSVM and other approaches mainly the SVM and LASVM shows the efficiency of our proposal.

Keywords

Incremental learning SVM Data stream 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.BESTMOD, Institut Supérieur de Gestion de TunisUniversité de TunisLe BardoTunisia

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