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Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection

Author

Listed:
  • Adel Fahad Alrasheedi

    (Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Khalid Abdulaziz Alnowibet

    (Statistics and Operations Research Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Akash Saxena

    (Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur 302017, India)

  • Karam M. Sallam

    (School of IT and Systems, University of Canberra, Bruce, ACT 2601, Australia)

  • Ali Wagdy Mohamed

    (Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt
    Department of Mathematics and Actuarial Science School of Sciences Engineering, The American University in Cairo, Cairo 11835, Egypt)

Abstract

Data mining applications are growing with the availability of large data; sometimes, handling large data is also a typical task. Segregation of the data for extracting useful information is inevitable for designing modern technologies. Considering this fact, the work proposes a chaos embed marine predator algorithm (CMPA) for feature selection. The optimization routine is designed with the aim of maximizing the classification accuracy with the optimal number of features selected. The well-known benchmark data sets have been chosen for validating the performance of the proposed algorithm. A comparative analysis of the performance with some well-known algorithms advocates the applicability of the proposed algorithm. Further, the analysis has been extended to some of the well-known chaotic algorithms; first, the binary versions of these algorithms are developed and then the comparative analysis of the performance has been conducted on the basis of mean features selected, classification accuracy obtained and fitness function values. Statistical significance tests have also been conducted to establish the significance of the proposed algorithm.

Suggested Citation

  • Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Akash Saxena & Karam M. Sallam & Ali Wagdy Mohamed, 2022. "Chaos Embed Marine Predator (CMPA) Algorithm for Feature Selection," Mathematics, MDPI, vol. 10(9), pages 1-18, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1411-:d:799973
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    Citations

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    Cited by:

    1. Savin Treanţă, 2022. "Variational Problems and Applications," Mathematics, MDPI, vol. 11(1), pages 1-4, December.
    2. Ahmed A. Ewees & Fatma H. Ismail & Rania M. Ghoniem & Marwa A. Gaheen, 2022. "Enhanced Marine Predators Algorithm for Solving Global Optimization and Feature Selection Problems," Mathematics, MDPI, vol. 10(21), pages 1-21, November.
    3. Akash Saxena & Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Ali Wagdy Mohamed, 2022. "A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines," Mathematics, MDPI, vol. 10(15), pages 1-16, August.
    4. Shoyab Ali & Annapurna Bhargava & Akash Saxena & Pavan Kumar, 2023. "A Hybrid Marine Predator Sine Cosine Algorithm for Parameter Selection of Hybrid Active Power Filter," Mathematics, MDPI, vol. 11(3), pages 1-25, January.

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