Panday, D and Amorim, RC and Lane, P (2018) 'Feature weighting as a tool for unsupervised feature selection.' Information Processing Letters, 129. pp. 44-52. ISSN 0020-0190

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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.

Item Type:	Article
Uncontrolled Keywords:	Feature selection; Clustering; Feature weighting
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	22 Sep 2017 13:50
Last Modified:	15 Jan 2022 01:20
URI:	http://repository.essex.ac.uk/id/eprint/20406

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