University of Hertfordshire

By the same authors

Feature weighting as a tool for unsupervised feature selection

Research output: Contribution to journalArticlepeer-review


View graph of relations
Original languageEnglish
Pages (from-to)44-52
Number of pages9
JournalInformation Processing Letters
Early online date21 Sep 2017
Publication statusPublished - 1 Jan 2018


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.


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.

ID: 12490886