دانلود مقاله انگلیسی رایگان:یک دیدگاه ترکیبی جدید برای انتخاب ویژگی و انتخاب مدل ماشینی برداری پشتیبانی برمبنای هوش هم گروه خود - منطبق - 2017
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  • A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence
    A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence

    سال انتشار:

    2017


    عنوان انگلیسی مقاله:

    A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence


    ترجمه فارسی عنوان مقاله:

    یک دیدگاه ترکیبی جدید برای انتخاب ویژگی و انتخاب مدل ماشینی برداری پشتیبانی برمبنای هوش هم گروه خود - منطبق


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 88 (2017) 118-131. doi:10.1016/j.eswa.2017.06.030


    نویسنده:

    Mohammed Aladeemy∗, Salih Tutun, Mohammad T. Khasawneh


    چکیده انگلیسی:

    This research proposes a new hybrid approach for feature selection and Support Vector Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) algorithm. Feature selection can improve the accuracy of classification algorithms and reduce their computation complexity by removing the irrelevant and redundant features. SVM is a classification algorithm that has been used in many ar eas, such as bioinformatics and pattern recognition. However, the classification accuracy of SVM depends mainly on tuning its hyperparameters (i.e., SVM model selection). This paper presents a framework that is comprised of the following two major components. First, Self-Adaptive Cohort Intelligence (SACI) algo rithm is proposed, which is a new variation of the emerging metaheuristic algorithm, Cohort Intelligence (CI). Second, SACI is integrated with SVM resulting in a new hybrid approach referred to as SVM–SACI for simultaneous feature selection and SVM model selection. SACI differs from CI by employing tournament based mutation and self-adaptive scheme for sampling interval and mutation rate. Furthermore, SACI is both real-coded and binary-coded, which makes it directly applicable to both binary and continuous do mains. The performance of SACI for feature selection and SVM model selection was examined using ten benchmark datasets from the literature and compared with those of CI and five well-known metaheuris tics, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC). The comparative results demonstrate that SACI outperformed CI and compa rable to or better than the other compared metaheuristics in terms of the SVM classification accuracy and dimensionality reduction. In addition, SACI requires less tuning efforts as the number of its control parameters is less than those of the other compared metaheuristics due to adopting the self-adaptive scheme in SACI. Finally, this research suggests employing more efficient methods for high-dimensional or large datasets due to the relatively high training time required by search strategies based on metaheuris tics when applied to such datasets.
    Keywords: Feature selection | SVM | Classification | Cohort intelligence | Metaheuristic


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 14
    حجم فایل: 2022 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




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