Please use this identifier to cite or link to this item:
DC FieldValueLanguage
dc.contributor.authorMafarja, Majdi
dc.contributor.authorSabar, Nasser R.
dc.description.abstractFeature selection (FS) is an important and challenging task in machine learning. FS can be defined as the process of finding the best informative subset of features in order to avoid the curse of dimensionality and maximise the classification accuracy. In this work, we propose a FS algorithm based on binary particle swarm optimisation (PSO) and $k$-NN classifier. PSO is a well-known swarm intelligent algorithm that have shown to be very effective in dealing with various difficult problems. Nevertheless, the performance of PSO is highly effected by the inertia weight parameter which controls the balance between exploration and exploitation. To address this issue, we use an adaptive mechanism to adaptively change the value of the inertia weight parameter based on the search status. The proposed PSO has been tested on 12 well-known datasets from UCI repository. The results show that the proposed PSO outperformed the other methods in terms of the number of features and classification accuracy.en_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectPattern recognition systemsen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectMathematical optimizationen_US
dc.subject.lcshSwarm intelligence
dc.titleRank based binary particle swarm optimisation for feature selection in classificationen_US
dc.typeConference Proceedingsen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.conferenceInternational Conference on Future Networks and Distributed Systems (2018 : Amman)en_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
item.fulltextWith Fulltext-
Appears in Collections:Fulltext Publications
Files in This Item:
File Description SizeFormat
Rank Based Binary Particle Swarm Optimisation for Feature Seelction.pdf464.15 kBAdobe PDFView/Open
Show simple item record

Page view(s)

Last Week
Last month
checked on May 11, 2022


checked on May 11, 2022

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.