BAHA: Binary artificial hummingbird algorithm for feature selection

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ali Hamdipour , Abdolali Basiri , Mostafa Zaare , Seyedali Mirjalili
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引用次数: 0

Abstract

Datasets classification accuracy depends on their features. The presence of irrelevant and redundant features in the dataset leads to the reduction of classification accuracy. Identifying and removing such features is the main purpose in feature selection, which is an important step in the data science lifecycle. The objective of the Wrapper feature selection method is to reduce the number of selected feature (NSF) while improving the classification accuracy by working on a set of features. The feature selection is a challenging and computationally expensive problem that falls under the NP-complete category, so it requires computationally cheap and efficient algorithm to solve it. The artificial hummingbird algorithm (AHA) is a biological inspired optimization technique that mimics the unique flight capabilities and intelligent foraging tactics of hummingbirds in nature. Since feature selection is inherently a binary problem. In this paper, the binary form of the AHA meta-heuristic algorithm is proposed to show that binarizing the AHA meta-heuristic algorithm improves its performance for solving feature selection problems. The proposed method is tested on a standard benchmark dataset and compared with four state-of-the-art feature selection algorithms: Automata-based improved equilibrium optimizer with U-shaped transfer function (AIEOU), Whale optimization approaches for wrapper feature selection (WOA-CM), Ring theory-based harmony search (RTHS), and Adaptive switching gray-whale optimizer (ASGW). The results show the effectiveness of the proposed algorithm in searching for optimal features subset. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/baha.
用于特征选择的二元人工蜂鸟算法
数据集的分类精度取决于它们的特征。数据集中不相关和冗余特征的存在会导致分类精度的降低。识别和删除这些特征是特征选择的主要目的,这是数据科学生命周期中的一个重要步骤。Wrapper特征选择方法的目标是减少被选特征(NSF)的数量,同时通过处理一组特征来提高分类精度。特征选择是一个具有挑战性且计算成本高的问题,属于np完全范畴,因此需要计算成本低且高效的算法来解决。人工蜂鸟算法(artificial hummingbird algorithm, AHA)是一种模拟自然界蜂鸟独特的飞行能力和智能觅食策略的仿生优化技术。因为特征选择本质上是一个二元问题。本文提出了AHA元启发式算法的二值化形式,表明对AHA元启发式算法进行二值化可以提高其解决特征选择问题的性能。该方法在标准基准数据集上进行了测试,并与四种最先进的特征选择算法进行了比较:基于自动机的u形传递函数改进均衡优化器(AIEOU)、用于包装特征选择的鲸鱼优化方法(WOA-CM)、基于环理论的和谐搜索(RTHS)和自适应切换灰鲸优化器(ASGW)。结果表明,该算法在搜索最优特征子集方面是有效的。该算法的源代码可以在https://github.com/alihamdipour/baha上公开。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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