Show simple item record

dc.contributor.authorMafarja, Majdi
dc.contributor.authorEleyan, Derar
dc.contributor.authorAbdullah, Salwani
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2021-09-14T03:28:54Z
dc.date.available2021-09-14T03:28:54Z
dc.date.issued2017
dc.identifier.isbn9781450348447
dc.identifier.doi10.1145/3102304.3102325
dc.identifier.urihttp://hdl.handle.net/10072/368941
dc.description.abstractFeature selection is an important preprocessing step for classification problems. It deals with selecting near optimal features in the original dataset. Feature selection is an NP-hard problem, so meta-heuristics can be more efficient than exact methods. In this work, Ant Lion Optimizer (ALO), which is a recent metaheuristic algorithm, is employed as a wrapper feature selection method. Six variants of ALO are proposed where each employ a transfer function to map a continuous search space to a discrete search space. The performance of the proposed approaches is tested on eighteen UCI datasets and compared to a number of existing approaches in the literature: Particle Swarm Optimization, Gravitational Search Algorithm and two existing ALO-based approaches. Computational experiments show that the proposed approaches efficiently explore the feature space and select the most informative features, which help to improve the classification accuracy.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameInternational Conference on Future Networks and Distributed Systems (ICFNDS)
dc.relation.ispartofconferencetitleICFNDS '17: Proceedings of the International Conference on Future Networks and Distributed Systems
dc.relation.ispartofdatefrom2017
dc.relation.ispartofdateto2017-07-20
dc.relation.ispartoflocationCambridge, UK
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto7
dc.relation.ispartofvolume21
dc.subject.fieldofresearchNetwork engineering
dc.subject.fieldofresearchcode400604
dc.titleS-Shaped vs. V-Shaped Transfer Functions for Ant Lion Optimization Algorithm in Feature Selection Problem
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ICFNDS '17: International Conference on Future Networks and Distributed Systems, ISBN: 978-1-4503-4844-7, https://doi.org/10.1145/3102304.3102325
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record