dc.contributor.author | Panday, Deepak | |
dc.contributor.author | Cordeiro De Amorim, Renato | |
dc.contributor.author | Lane, Peter | |
dc.date.accessioned | 2018-01-31T12:21:04Z | |
dc.date.available | 2018-01-31T12:21:04Z | |
dc.date.issued | 2018-01-01 | |
dc.identifier.citation | Panday , D , Cordeiro De Amorim , R & Lane , P 2018 , ' Feature weighting as a tool for unsupervised feature selection ' , Information Processing Letters , vol. 129 , pp. 44-52 . https://doi.org/10.1016/j.ipl.2017.09.005 | |
dc.identifier.uri | http://hdl.handle.net/2299/19699 | |
dc.description | This document is the Accepted Manuscript version of the following article: Deepak Panday, Renato Cordeiro de Amorin, and Peter Lane, ‘Feature weighting as a tool for unsupervised feature selection’, Information Processing Letters, Vol. 129, January 2018. Under embargo. Embargo end date: 21 September 2018. Published by Elsevier. | |
dc.description.abstract | Feature selection is a popular data pre-processing step. The aim is to remove some of the features in a data set with minimum information loss, leading to a number of benefits including faster running time and easier data visualisation. In this paper we introduce two unsupervised feature selection algorithms. These make use of a cluster-dependent feature-weighting mechanism reflecting the within-cluster degree of relevance of a given feature. Those features with a relatively low weight are removed from the data set. We compare our algorithms to two other popular alternatives using a number of experiments on both synthetic and real-world data sets, with and without added noisy features. These experiments demonstrate our algorithms clearly outperform the alternatives. | en |
dc.format.extent | 9 | |
dc.format.extent | 543214 | |
dc.language.iso | eng | |
dc.relation.ispartof | Information Processing Letters | |
dc.subject | Algorithms | |
dc.subject | Clustering | |
dc.subject | Feature selection | |
dc.subject | Theoretical Computer Science | |
dc.subject | Signal Processing | |
dc.subject | Information Systems | |
dc.subject | Computer Science Applications | |
dc.title | Feature weighting as a tool for unsupervised feature selection | en |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Biocomputation Research Group | |
dc.description.status | Peer reviewed | |
dc.date.embargoedUntil | 2018-09-21 | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85030177010&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1016/j.ipl.2017.09.005 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |