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An Intelligent Metaheuristic Binary Pigeon Optimization-Based Feature Selection and Big Data Classification in a MapReduce Environment

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  • Felwa Abukhodair

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Wafaa Alsaggaf

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Amani Tariq Jamal

    (Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Sayed Abdel-Khalek

    (Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
    Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt)

  • Romany F. Mansour

    (Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt)

Abstract

Big Data are highly effective for systematically extracting and analyzing massive data. It can be useful to manage data proficiently over the conventional data handling approaches. Recently, several schemes have been developed for handling big datasets with several features. At the same time, feature selection (FS) methodologies intend to eliminate repetitive, noisy, and unwanted features that degrade the classifier results. Since conventional methods have failed to attain scalability under massive data, the design of new Big Data classification models is essential. In this aspect, this study focuses on the design of metaheuristic optimization based on big data classification in a MapReduce (MOBDC-MR) environment. The MOBDC-MR technique aims to choose optimal features and effectively classify big data. In addition, the MOBDC-MR technique involves the design of a binary pigeon optimization algorithm (BPOA)-based FS technique to reduce the complexity and increase the accuracy. Beetle antenna search (BAS) with long short-term memory (LSTM) model is employed for big data classification. The presented MOBDC-MR technique has been realized on Hadoop with the MapReduce programming model. The effective performance of the MOBDC-MR technique was validated using a benchmark dataset and the results were investigated under several measures. The MOBDC-MR technique demonstrated promising performance over the other existing techniques under different dimensions.

Suggested Citation

  • Felwa Abukhodair & Wafaa Alsaggaf & Amani Tariq Jamal & Sayed Abdel-Khalek & Romany F. Mansour, 2021. "An Intelligent Metaheuristic Binary Pigeon Optimization-Based Feature Selection and Big Data Classification in a MapReduce Environment," Mathematics, MDPI, vol. 9(20), pages 1-14, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2627-:d:658917
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    References listed on IDEAS

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    1. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    2. Harish Garg & Muhammad Riaz & Muhammad Abdullah Khokhar & Maryam Saba, 2021. "Correlation Measures for Cubic m-Polar Fuzzy Sets with Applications," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-19, September.
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    1. Nahla Mohammed Elzein & Mazlina Abdul Majid & Ibrahim Abaker Targio Hashem & Ashraf Osman Ibrahim & Anas W. Abulfaraj & Faisal Binzagr, 2023. "JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique," Mathematics, MDPI, vol. 11(5), pages 1-20, March.

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