Novel consensus approaches to the reliable ranking of features for seabed imagery classification

Harrison, Richard, Birchall, Roger, Mann, Dave and Wang, Wenjia (2012) Novel consensus approaches to the reliable ranking of features for seabed imagery classification. International Journal of Neural Systems, 22 (6). ISSN 0129-0657

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Abstract

Feature saliency estimation and feature selection are important tasks in machine learning applications. Filters, such as distance measures are commonly used as an efficient means of estimating the saliency of individual features. However, feature rankings derived from different distance measures are frequently inconsistent. This can present reliability issues when the rankings are used for feature selection. Two novel consensus approaches to creating a more robust ranking are presented in this paper. Our experimental results show that the consensus approaches can improve reliability over a range of feature parameterizations and various seabed texture classification tasks in sidescan sonar mosaic imagery.

Item Type:	Article
Uncontrolled Keywords:	feature ranking,distance measures,consensus methods,seabed texture classification
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Pure Connector
Date Deposited:	18 Aug 2014 15:22
Last Modified:	21 Oct 2022 00:05
URI:	https://ueaeprints.uea.ac.uk/id/eprint/49924
DOI:	10.1142/S0129065712500268

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