AN EFFICIENT PROCESSİNG-İN-MEMORY COMPUTİNG USING HYBRID CONSENSUS LEARNING BASE-CLASSIFIERS AND CONS MODEL

Abstract

This paper proposes  optimization  processed for Min-Max normalization that helped alleviating convergence and over-fitting problem. The normalized features were processed for two-class classification using nine machine learning algorithm including Naïve Bayes variants regression techniques, pattern mining, association rule mining, pattern mining, association rule mining, neuro-computing and ensemble learning. These nine base-classifiers constituted a robust heterogenous ensemble learning environment which labelled each SQL-query as the normal traffic or SQLIA and thus based on the maximum voting score our proposed consensus (CONS) model predicted each query as the Normal SQL-traffic or the SQLIA., The resampled features along with the original features were processed for feature selection using significant predictor test and variance threshold feature selection (VTFS) algorithms so as to improve time-efficiency as well as to reduce redundant computation. The selected feature.