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Assessment of Fuzzy Gaussian Naive Bayes for Classification Tasks

Authors:
Jodavid Ferreira
Elaine Soares
Liliane Machado
Ronei Moraes

Keywords: Fuzzy Gaussian Naive Bayes Classifier, Classification, Accuracy Assessment

Abstract:
Statistical methods have been used in order to classify data from random samples. In general, if we know the statistical distribution of data, we can use specific classifiers designed for that distribution and expect good results. This work assesses the accuracy of a Fuzzy Gaussian Naive Bayes (FGNB) classifier for tasks using data from five different statistical distributions: Negative Binomial, Logistic, Log-Normal, Weibull and Gamma. The FGNB classifier was recently proposed as a fuzzy extension of Gaussian Naive Bayes for training assessment in virtual environments. Results of assessment are provided and show different accuracy according to the statistical distribution of data.

Pages: 64 to 69

Copyright: Copyright (c) IARIA, 2015

Publication date: March 22, 2015

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-61208-393-3

Location: Nice, France

Dates: from March 22, 2015 to March 27, 2015