نوع مقاله : مقاله پژوهشی
نویسندگان
گروه علوم کامپیوتر، دانشکده ریاضی و علوم کامپیوتر، دانشگاه دامغان، دامغان، ایران.
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The classification accuracy of datasets heavily depends on their features. The presence of irrelevant and redundant features in a dataset can lead to a reduction in classification accuracy. Identifying and remov-ing such features is the main purpose of feature selection problem, which is an important step in the data science lifecycle. The aim of the Wrapper feature selection method is to reduce the number of selected features (SF) while improving the classification accuracy by optimizing a set of features. Feature selec-tion is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally efficient algorithms to solve it. The Artificial Rabbits Optimization (ARO) is a biologically inspired optimization technique that mimics the unique and intelligent foraging tactics of rabbits in nature. This paper proposed a new feature selection method based on the ARO meta-heuristic algorithm, called the memory artificial rabbits optimization (MARO), to improve its performance for solving feature selection problems. The proposed MARO method is tested on a standard benchmark da-taset and compared with four state-of-the-art feature selection algorithms. The results show the effec-tiveness of the proposed MARO algorithm in searching for an optimal subset of features.
کلیدواژهها [English]
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