@inproceedings{f45337099cc0441bb71efda7cfe4255e,
title = "Binary Dragonfly Algorithm for Feature Selection",
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.",
keywords = "classification, Dragonfly Algorithm, Optimization, Selection",
author = "Mafarja, {Majdi M.} and Derar Eleyan and Iyad Jaber and Abdelaziz Hammouri and Seyedali Mirjalili",
year = "2018",
month = jan,
day = "8",
doi = "10.1109/ICTCS.2017.43",
language = "English",
series = "Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "12--17",
editor = "Arafat Awajan and Adnan Shaout",
booktitle = "Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017",
note = "2017 International Conference on New Trends in Computing Sciences, ICTCS 2017 ; Conference date: 11-10-2017 Through 13-10-2017",
}