Research Article

Novel RVM Approach to Structuring and Classifying Epidemic Outbreak Data

by  Sunaina Sharma, Veenu Mangat
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Issue 14
Published: October 2015
Authors: Sunaina Sharma, Veenu Mangat
10.5120/ijca2015906588
PDF

Sunaina Sharma, Veenu Mangat . Novel RVM Approach to Structuring and Classifying Epidemic Outbreak Data. International Journal of Computer Applications. 127, 14 (October 2015), 40-45. DOI=10.5120/ijca2015906588

                        @article{ 10.5120/ijca2015906588,
                        author  = { Sunaina Sharma,Veenu Mangat },
                        title   = { Novel RVM Approach to Structuring and Classifying Epidemic Outbreak Data },
                        journal = { International Journal of Computer Applications },
                        year    = { 2015 },
                        volume  = { 127 },
                        number  = { 14 },
                        pages   = { 40-45 },
                        doi     = { 10.5120/ijca2015906588 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2015
                        %A Sunaina Sharma
                        %A Veenu Mangat
                        %T Novel RVM Approach to Structuring and Classifying Epidemic Outbreak Data%T 
                        %J International Journal of Computer Applications
                        %V 127
                        %N 14
                        %P 40-45
                        %R 10.5120/ijca2015906588
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Classifying this indefinite big data, is computationally intensive as a large amount of data is related with an existential probability of undefined or undetermined values of raw data. Classifying is hindered by a large amount of data from various sources. RVM, a Bayesian formulation of the linear model both for classification and regression, has lately involved a lot of interest in the research community. The paper aims at learning kernelized RVM classifier to evaluate Ebola virus outbreak, using generalization error, intra class separability, missing probability Pi is compared to SVM.RVM relevance impact with other epidemic diseases of Ebola Virus is also compared.

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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

classification relevance vector machine support vector machine Naive Bayes neural network generalization error intra class separarbility missing probability Predictive value imputation distributed based imputation

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