Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5316
Title: Binary dragonfly algorithm for feature selection
Authors: Mafarja, Majdi
Jaber, Iyad
Eleyan, Derar
Hammouri, Abdelaziz
Mirjalili, Seyedali
Keywords: Artificial Intelligence
Data mining
Computational intelligence
Machine learning
Selection theorems
Rough sets
Mathematical optimization Binary dragonfly algorithm
Binary dragonfly algorithm
Issue Date: 13-Oct-2017
Publisher: International Conference on new Trends in Computing Sciences (2017 : Amman, JO)
Abstract: Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of the dragonflies. Eighteen UCI datasets are used to evaluate the performance of the proposed approach. The results of the proposed method are compared with those of Particle Swarm Optimization (PSO), Genetic Algorithms (GAs) in terms of classification accuracy and number of selected attributes. The results show the ability of Binary Dragonfly Algorithm (BDA) in searching the feature space and selecting the most informative features for classification tasks.
URI: http://hdl.handle.net/20.500.11889/5316
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