Abstract
This paper presents a combined solution for Big Data classification, by using one of the extended versions of the Support Vector Machines (SVM), known as the Parallel Support Vector Machines (PSVM). The main problem assumes that, once a PSVM model is obtained, a feature can be removed overtime, resulting in a decrease of the accuracy with the existing model. While Big Data is one of the interesting contexts, then training a new PSVM with the new data structure is time-consuming. The solution is to use an approach that approximates any SVM model based on the Radial Basis Function (RBF) kernel, and called the Approx SVM. In order to avert a new training step, this paper proposes to apply the Approx SVM in a parallel architecture. Despite that the Approx SVM was not purposely used to deal with large-scaled data set, the experimental results, which will be presented at the end of the article, are proofs that this approach is an appropriate choice for PSVM models.
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References
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)
Bordes, A., Ertekin, S., Weston, J., Bottou, J.: Fast kernel classifiers with online and active learning. J. Mach. Learn. Res. 6, 1579–1619 (2005)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. Adv. Neural Inf. Process. Syst. (NIPS*2000) 13, 409–415 (2000)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Real time support vector machines. Sci. Inf. SAI 2014, 496–501 (2014)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to supportvector classification, pp. 1–16 (2016)
Nalavade, K., Meshram, B.B.: Data Classification Using Support Vector Machines. In: National Conference on Emerging Trends in Engineering and Technology (VNCET), vol. 2 (2012)
Cornuéjols, A., Miclet, L.: Apprentissage artificiel: concepts et algorithmes. Editions Eyrolles (2011)
Fletcher, T.: Support vector machines explained. Tutorial paper (2009)
Sewell, M.: SVMdark: A Windows Implementation of a Support Vector Machine. UCL, London (2005)
Priyadarshini, A.: A map reduce based support vector machine for big data classification. Int. J. Database Theor. Appl. 8(5), 77–98 (2015)
Joachims, T.: Making large-scale SVM learning practical. Technical report, SFB 475: Komplexittsreduktion in Multivariaten Datenstrukturen, No. 1998, 28. Universitt Dortmund (1998)
Collobert, R., Bengio, S., Marithoz, J.: Torch: a modular machine learning software library. No. EPFL-REPORT-82802. Idiap (2002)
Graf, H.P., et al.: Parallel support vector machines: the cascade SVM. In: NIPS, Vol. 17 (2004)
Claesen, M., et al.: Fast prediction with SVM models containing RBF kernels. arXiv preprint (2014). arXiv:1403.0736
Vert, J.-P., Tsuda, K., Schlkopf, B.: A primer on kernel methods. In: Kernel Methods in Computational Biology, pp. 35–70 (2004)
Maji, S., Berg, A.C., Malik, J.: Efficient classification for additive kernel SVMs. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 66–77 (2013)
Lemaire, V., Salperwyck, C., Bondu, A.: A survey on supervised classification on data streams. In: Proceedings of Business Intelligence: 4th European Summer School, EBISS, pp. 88-125 (2015)
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Ksiaâ, W., Rejab, F.B., Nouira, K. (2018). Big Data Classification: A Combined Approach Based on Parallel and Approx SVM. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_43
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DOI: https://doi.org/10.1007/978-3-319-59480-4_43
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