Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19492
Title: A Resource Aware Parallelized Back Propagation Neural Network in Enabling Efficient Large-Scale Digital Health Data Processing
Authors: Liu, Y
Chen, X
Xu, L
Li, H
Li, M
Keywords: back propagation neural network;parallelization;hadoop;gene expression programming;load balancing
Issue Date: 15-Aug-2019
Publisher: Institute of Electrical and Electronics Engineers
Citation: IEEE Access, 2019, 7 pp. 114700 - 114713
Abstract: Along with the development of digital health, efficient machine learning is anxiously needed to handle the growing health data. Among various machine learning algorithms, back propagation neural network (BPNN) shows great effectiveness in both academia and industrial fields. However, it is frequently reported that the conventional BPNN algorithm encounters low efficiency issue in dealing with large-scale digital health data. Therefore this paper presents a Hadoop based parallelized BPNN algorithm which is able to process the large-scale data efficiently. In order to complement the potential accuracy loss issue for the parallelized data processing, ensemble learning techniques are also involved. Additionally although Hadoop supplies a number of default schedulers, the heterogeneous distributed computing environment may still impact the efficiency of the parallelized BPNN. Consequently, this paper also presents a gene expression programming (GEP) algorithm based load balancing approach, which enables the computing resource awareness and the optimal scheduling of the parallelized BPNN. The experiments employ the classification task as the underlying testing basis. Two types of the experiments are carried out, in which the first one focuses on evaluating the accuracy of the presented algorithm with classifying the benchmark dataset; the second one focuses on evaluating the efficiency of the presented algorithm with classifying the large-scale dataset. The experimental results show the effectiveness of the presented resource aware parallelized BPNN algorithm.
URI: https://bura.brunel.ac.uk/handle/2438/19492
DOI: https://doi.org/10.1109/access.2019.2935691
ISSN: 2169-3536
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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