Binary Dragonfly Algorithm for Feature Selection

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 sw...

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Vydáno v:	2017 International Conference on New Trends in Computing Sciences (ICTCS) s. 12 - 17
Hlavní autoři:	Mafarja, Majdi M., Eleyan, Derar, Jaber, Iyad, Hammouri, Abdelaziz, Mirjalili, Seyedali
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.10.2017
Témata:	classification Classification algorithms Dragonfly Algorithm Feature extraction Genetic algorithms Mathematical model MIMICs Optimization Particle swarm optimization Selection
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Popis
Shrnutí:	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.
DOI:	10.1109/ICTCS.2017.43