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  • Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm

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    MirjaliliPUB6338.pdf (943.1Kb)
    Author(s)
    Aljarah, Ibrahim
    Al-Zoubi, Ala M
    Faris, Hossam
    Hassonah, Mohammad A
    Mirjalili, Seyedali
    Saadeh, Heba
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2018
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    Abstract
    Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In ...
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    Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features.
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    Journal Title
    Cognitive Computation
    Volume
    10
    Issue
    3
    DOI
    https://doi.org/10.1007/s12559-017-9542-9
    Copyright Statement
    © 2018 Springer US. This is an electronic version of an article published in Cognitive Computation, June 2018, Volume 10, Issue 3, pp 478–495. Cognitive Computation is available online at: http://link.springer.com// with the open URL of your article.
    Subject
    Artificial intelligence
    Neurosciences
    Cognitive and computational psychology
    Publication URI
    http://hdl.handle.net/10072/379937
    Collection
    • Journal articles

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