MF - Ensemble Algorithm with threshold to enhance the Supervised Learning Classifier Accuracy on Big Data

Abstract

The increasing rate of data volume causes an issue in data management and decisions making. Big data analytics furnishes a data analysis in an automated way with the consideration of techniques such as classification. The data classification attained performance in less and computational challenges as high in the directions of big data. The reason behind this issue is focused on high data volume and dimensions. To enhance the performance on the prediction of classification task with the imputes of classification models, the dimensional in advanced linear perspective needs an effective approach to reduce the dimensions of data. An effective approach considered in this research work is the ensemble strategy which aims to identify the optimal set of features. This strategy is to tackle the big data difficulty especially with high dimensionality. Hence, the solution of optimum feature set to improve the classification performance is an of the essence challenge. A novel method i.e. ensemble method has been considered as an efficient conceptualization to better the accuracy of classification.