Description

In this paper we propose Large Iterative Multitier Ensemble (LIME) classifiers built explicit for processing Big data. The main aim of develop LIME classifiers as a general technique that may be useful for the analysis of Big Data in various application domains. In this large iterative has an four tier classifier and it analyse the data security. Then iterative classifier usage of one classifier integral to another ensemble meta classifier. The LIME classifier has an five tier it performing more security for data transferring from one user to another. Many ensemble classifier process as function to higher tier ensemble classifier which results increase in accuracy for processing. Traditional ensemble meta classifiers generate their collection of base classifiers given an indication, template of only one base classifier as an input parameter.