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Comparison between Fisher's Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition

Journal of physics. Conference series, 2021-02, Vol.1752 (1), p.12042 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2021. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1752/1/012042

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  • Title:
    Comparison between Fisher's Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition
  • Author: Rustam, Z ; Kristina, Andrea Laksmirani ; Satria, Y
  • Subjects: Classification ; Classifiers ; Face recognition ; Fisher's ratio ; Machine learning ; Physics ; recognition ; Support vector machines ; SVM classifier
  • Is Part Of: Journal of physics. Conference series, 2021-02, Vol.1752 (1), p.12042
  • Description: The enthusiasm level of a person is an important measurement in real-world problems. This paper, therefore, presents face recognition classification for enthusiasm level, based on supervised machine learning, using Support Vector Machine (SVM) as a classifier, with a one-vs-one method because the data consists of more than two classes. In addition, Fisher's Ratio and Information Gain are applied in the selection of contributive features, and the goals were to present an accuracy comparison between SVM and Fisher's Ratio, as wells as with Information Gain, and the results showed the accuracy at 88,89%, and 80,95238%, respectively. This indicates the combination of SVM with Fisher Ratio to be better.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1752/1/012042
  • Source: Geneva Foundation Free Medical Journals
    IOP Publishing (Open access)
    IOPscience (Open Access)
    ProQuest Central
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