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    A Binary classifier based on Firefly Algorithm

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    Date
    Dec 2017
    Author
    Al-Abdallah, Raed Z.
    Ameera S. Jaradat
    Abu Doush, Iyad
    Jaradatm, Yazan
    Type
    Journal Article
    Peer-Reviewed
    Metadata
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    Abstract
    This work implements the Firefly algorithm (FA) to find the best decision hyper-plane in the feature space. The proposed classifier uses a cross-validation of a 10-fold portioning for the training and the testing phases used for classification. Five pattern recognition binary benchmark problems with different feature vector dimensions are used to demonstrate the effectiveness of the proposed classifier. We compare the FA classifier results with those of other approaches through two experiments. The experimental results indicated that FA classifier is a competitive classification technique. The FA shows better results in three out of the four tested datasets used in the second experiment
    URI
    http://hdl.handle.net/11675/5025
    External link
    http://www.jjcit.org/Volume%2003,%20Number%2003/3-DOI%2010.5455-jjcit.71-1501152301.pdf
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    • College of Engineering and Applied Sciences [148]

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