Conference Paper2021

Comparing recent Swarm Algorithms with Information Theoretic Filter criterion for Feature Selection

Hasnain Hossain, Tahmid Bin Mahmud, AKM Mahbubur Rahman, M Ashraful Amin, Amin Ahsan Ali

2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)

IEEE, pp. 1–6

DOI: 10.1109/icecet52533.2021.9698430URL

Abstract

Feature selection is getting significant importance in the fields of machine learning specially for dealing with high dimensional datasets. Feature selection techniques such as swarm algorithms have proven to be very effective in extracting meaningful information from large amounts of high dimensional data, thus improving prediction performance and reducing computation time. In this research, we evaluate the effectiveness of introducing mRMR (minimum Redundancy-Maximum Rel-evance) as an information theoretic filtering criterion on top of swarm optimization algorithms to further improve on existing techniques. We also present a comparison of the performances of two recent swarm algorithms against the well-known Artificial Bee Colony algorithm after incorporating mRMR-based filtering to demonstrate the applicability of this technique.

Keywords

  • Feature selection
  • minimum redundancy maximum relevance
  • high-dimensional data
  • artificial bee colony algorithm

CCDS Authors

Hasnain Hossain
Hasnain Hossain

Hasnain Hossain

  • Alumni, CCDS

  • Master's Student

    University of Adelaide, Australia

  • System Analyst

    CholPori

  • BSc

    Independent University, Bangladesh (IUB)

Tahmid Bin Mahmud
Tahmid Bin Mahmud

Tahmid Bin Mahmud

  • Alumni, CCDS

  • Senior Research Associate

    BRAC Institute of Governance and Development, Bangladesh

  • BSc

    Independent University, Bangladesh (IUB)

AKM Mahbubur Rahman, PhD
AKM Mahbubur Rhman, PhD

AKM Mahbubur Rahman, PhD

  • Director, DS & IPT Wings

  • Associate Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

CHI, ECCV, WACV, PAKDD, LREC, IJCNN, ICPR, ICIP, HCII, BEA, Scientific Reports, Information Sciences

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Prof. Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

  • Director, AI/ML Wing
  • Supervisor, ComBio Wing

  • Professor

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

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