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Performance analysis of statistical classifier SMO with other data mining classifiers
chapterposted on 2017-12-06, 00:00 authored by A B M Shawkat Ali
Seven classifiers are compared on sixteen quite different, standard and extensively used datasets in terms of classification error rates and computational times. It is found that the average error rates for a majority of the classifiers are closes with each other but the computational times of the classifiers differ over a wide range. The statistical classifier Sequential Minimal Optimization (SMO) based on Support Vector Machine has the lowest average error rate and computationally it is faster than four classifiers but slightly expensive than other two classifiers.
Parent TitleAdvances in soft computing : engineering design and manufacturing
Number of Pages7
Place of PublicationLondon, UK
External Author AffiliationsMonash University;