Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features

机译：独特的蜻蜓优化算法，用于获取和聚类关键特征

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In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered “Sequential Forward Selection” for feature extraction

机译：在许多应用中，特征选择起着重要的作用，因为最佳特征可以产生准确的结果。所选特征必须代表整个数据集。在这里，我们考虑了用于特征提取的“顺序前向选择”

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来源
《IEEE International Conference on Cognitive Informatics and Cognitive Computing》|2019年|1 v.|共13页
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作者
Nagaraju Devarakonda; Sarvani Anandarao; Raviteja Kamarajugadda; Yingxu Wang;
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中图分类计算技术、计算机技术;
关键词
Dragonfly algorithm; sequential forward selection; convergence function; benchmark functions; Tornadogenesis Optimization algorithm; whale optimization algorithm;

机译：蜻蜓算法;顺序正向选择;收敛函数;基准函数;优化算法;鲸鱼优化算法;

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2. Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO) [J] . Farhan Aadil, Waleed Ahsan, Zahoor Ur Rehman, Journal of supercomputing . 2018,第9期

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3. Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification [J] . M. Hariharan, R. Sindhu, Vikneswaran Vijean, Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2018,第期

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5. Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features [C] . Nagaraju Devarakonda, Sarvani Anandarao, Raviteja Kamarajugadda, IEEE International Conference on Cognitive Informatics and Cognitive Computing . 2019

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Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features

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