Parallelized FPA-SVM: Parallelized parameter selection and classification using Flower Pollination Algorithm and Support Vector Machine

Abstract

Support Vector Machine (SVM) is one of the most popular machine learning algorithm to perform classification tasks and help organizations in different ways to improve their efficiency. A lot of studies have been made to improve SVM including speed, accuracy, and/or scalability. The algorithm possesses parameters that need precision tuning to perform well. This work proposes a novel parallelized parameter selection using Flower Pollination Algorithm (FPA) to quickly find the optimal parameters of SVM. In particular, MapReduce algorithm introduced in big data framework is applied to both FPA and SVM, which forms a fully distributed algorithm to support a large dataset. The experimental results of Parallelized FPA-SVM on real datasets show its outstanding speed in generating optimal models while maintaining high accuracy.