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  • A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

    Author(s)
    Faris, Hossam
    Hassonah, Mohammad A
    Al-Zoubi, Ala' M
    Mirjalili, Seyedali
    Aljarah, Ibrahim
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2018
    Metadata
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    Abstract
    Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters ...
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    Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy.
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    Journal Title
    Neural Computing and Applications
    DOI
    https://doi.org/10.1007/s00521-016-2818-2
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Artificial Intelligence and Image Processing not elsewhere classified
    Artificial Intelligence and Image Processing
    Electrical and Electronic Engineering
    Cognitive Sciences
    Publication URI
    http://hdl.handle.net/10072/345128
    Collection
    • Journal articles

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