Abstract
Machine Learning (ML) on massive scale datasets, called Big Data, has become a challenge for traditional computing and storage technologies. Henceforth, massive scale ML is an emerging domain of research. Least Square Twin Support Vector Machine (LSTSVM) is a faster variant of Support Vector Machine (SVM). However, it suffers from scalability issues and shows computational and/or storage bottlenecks on massive datasets. Proposed work designs a scalable solution to LSTSVM called Distributed LSTSVM (DLSTSVM). DLSTSVM is designed using distributed parallel computing on top of cluster of multiple machines. After applying horizontal partitioning on massive datasets, DLSTSVM trains it in distributed parallel fashion and finds two non-parallel hyper-planes as decision boundaries for two different classes. MapReduce paradigm is utilized to execute parallel computation on partitioned data in a way that averts memory constraints. Proposed technique achieves computational and storage scalability without losing prediction accuracy.
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Prasad, B.R., Agarwal, S. (2019). Scalable Least Square Twin Support Vector Machine Learning. In: Ordonez, C., Song, IY., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2019. Lecture Notes in Computer Science(), vol 11708. Springer, Cham. https://doi.org/10.1007/978-3-030-27520-4_17
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DOI: https://doi.org/10.1007/978-3-030-27520-4_17
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