Van Dyk, EBarnard, E2008-01-242008-01-242007-11Van Dyk, E and Barnard, E. 2007. Naive Bayesian classifiers for multinomial features: a theoretical analysis. 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Pietermaritzburg, Kwazulu-Natal, South Africa, 28-30 November 2007, pp 6978-1-86840-656-2http://hdl.handle.net/10204/1977http://search.sabinet.co.za/WebZ/images/ejour/comp/comp_v40_a8.pdf:sessionid=0:bad=http://search.sabinet.co.za/ejour/ejour_badsearch.html:portal=ejournal:2007: PRASAThis paper is published in the South African Computer Journal, Vol 40, pp 37-43The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density functions for all multinomial likelihood functions describing different classes. They also develop a simplified method to account for the correlation between multinomial variables. With accurate estimates for the distributions of all the likelihood functions, the authors are able to calculate classification error estimates for any such multinomial likelihood classifier. This error estimate can be used for feature selection, since it is easy to predict the effect that different features have on the error rate performanceenBayesian classifiersMultinominal featuresConference PresentationVan Dyk, E., & Barnard, E. (2007). Naive Bayesian classifiers for multinomial features: a theoretical analysis. 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA). http://hdl.handle.net/10204/1977Van Dyk, E, and E Barnard. "Naive Bayesian classifiers for multinomial features: a theoretical analysis." (2007): http://hdl.handle.net/10204/1977Van Dyk E, Barnard E, Naive Bayesian classifiers for multinomial features: a theoretical analysis; 18th Annual Symposium of the Pattern Recognition Association of South Africa (PRASA); 2007. http://hdl.handle.net/10204/1977 .TY - Conference Presentation AU - Van Dyk, E AU - Barnard, E AB - The authors investigate the use of naive Bayesian classifiers for multinomial feature spaces and derive error estimates for these classifiers. The error analysis is done by developing a mathematical model to estimate the probability density functions for all multinomial likelihood functions describing different classes. They also develop a simplified method to account for the correlation between multinomial variables. With accurate estimates for the distributions of all the likelihood functions, the authors are able to calculate classification error estimates for any such multinomial likelihood classifier. This error estimate can be used for feature selection, since it is easy to predict the effect that different features have on the error rate performance DA - 2007-11 DB - ResearchSpace DP - CSIR KW - Bayesian classifiers KW - Multinominal features LK - https://researchspace.csir.co.za PY - 2007 SM - 978-1-86840-656-2 T1 - Naive Bayesian classifiers for multinomial features: a theoretical analysis TI - Naive Bayesian classifiers for multinomial features: a theoretical analysis UR - http://hdl.handle.net/10204/1977 ER -