Abstract
The paper presents our approach to SVM implementation in parallel environment. We describe how classification learning and prediction phases were pararellised. We also propose a method for limiting the number of necessary computations during classifier construction. Our method, named one-vs-near, is an extension of typical one-vs-all approach that is used for binary classifiers to work with multiclass problems. We perform experiments of scalability and quality of the implementation. The results show that the proposed solution allows to scale up SVM that gives reasonable quality results. The proposed one-vs-near method significantly improves effectiveness of the classifier construction.
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Balicki, J., Szymański, J., Kępa, M., Draszawka, K., Korłub, W. (2015). Improving Effectiveness of SVM Classifier for Large Scale Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_60
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DOI: https://doi.org/10.1007/978-3-319-19324-3_60
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
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