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.
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Ben Rejab, F., Nouira, K. (2018). Incremental Real Time Support Vector Machines. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_22
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DOI: https://doi.org/10.1007/978-3-319-76348-4_22
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