Ensemble Algorithms for Huge Datasets using Deep Learning

Abstract

Various intelligent systems are developed by the Deep Learning (DL). Researchers are more concentrating on processing of huge datasets using DL. Especially to process various pattern based datasets it is highly complicated to solve the issues. Due to the non-handling of the huge datasets k-nearest neighbors (KNN) is not fit for the huge pattern recognition datasets. Our investigation is using a pattern based classification on numerical dataset; here we are using Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition dataset. Principle component analysis (PCA) is used to reduce the dimensionality of the dataset and integrated with Ensemble Novel Classifier (ENC). Experimental results show the performance of the proposed system