Relevance Vector Machine classification for big data on Ebola outbreak

Currently, huge sizes of indeterminate data are effortlessly collected or created at a very high pace in numerous real-life applications. Classifying this indefinite big data, is computationally intensive as large amount of data is related with existential probability of undefined or undetermined values of raw data. In this study, we propose a data mining approach for the classification of big dataset based on death toll by epidemic outbreak of Ebola virus and comparing its relevance with other epidemic diseases and generalizing error and intra class separability using relevance vector machine classifier.