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دسته بندی:
بیوانفورماتیک - Bioinformatics
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
قیمت: رایگان
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