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 |
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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: | 19 Jul 2022 10:00 |
URI: | http://repository.essex.ac.uk/id/eprint/20406 |
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