University of Hertfordshire

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Feature weighting as a tool for unsupervised feature selection

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Feature weighting as a tool for unsupervised feature selection. / Panday, Deepak; Cordeiro De Amorim, Renato; Lane, Peter.

In: Information Processing Letters, Vol. 129, 01.01.2018, p. 44-52.

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Panday, Deepak ; Cordeiro De Amorim, Renato ; Lane, Peter. / Feature weighting as a tool for unsupervised feature selection. In: Information Processing Letters. 2018 ; Vol. 129. pp. 44-52.

Bibtex

@article{755e5fae08234a3a9f375291a1af5ab0,
title = "Feature weighting as a tool for unsupervised feature selection",
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 witha 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.",
keywords = "Algorithms, Clustering, Feature selection",
author = "Deepak Panday and {Cordeiro De Amorim}, Renato and Peter Lane",
note = "This document is the Accepted Manuscript version of the following article: Deepak Panday, Renato Cordeiro de Amorin, and Peter Lane, {\textquoteleft}Feature weighting as a tool for unsupervised feature selection{\textquoteright}, Information Processing Letters, Vol. 129, January 2018. Under embargo. Embargo end date: 21 September 2018. Published by Elsevier.",
year = "2018",
month = jan,
day = "1",
doi = "10.1016/j.ipl.2017.09.005",
language = "English",
volume = "129",
pages = "44--52",
journal = "Information Processing Letters",
issn = "0020-0190",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Feature weighting as a tool for unsupervised feature selection

AU - Panday, Deepak

AU - Cordeiro De Amorim, Renato

AU - Lane, Peter

N1 - 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.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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 witha 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.

AB - 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 witha 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.

KW - Algorithms

KW - Clustering

KW - Feature selection

UR - http://www.scopus.com/inward/record.url?scp=85030177010&partnerID=8YFLogxK

U2 - 10.1016/j.ipl.2017.09.005

DO - 10.1016/j.ipl.2017.09.005

M3 - Article

VL - 129

SP - 44

EP - 52

JO - Information Processing Letters

JF - Information Processing Letters

SN - 0020-0190

ER -