title: Novel consensus approaches to the reliable ranking of features for seabed imagery classification.
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.
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich, UK. richard.harrison@uea.ac.uk
authors.forename: Richard
authors.initials: R
authors.lastname: Harrison
authors.forename: Roger
authors.initials: R
authors.lastname: Birchall
authors.forename: Dave
authors.initials: D
authors.lastname: Mann
authors.forename: Wenjia
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000145:classification* / Q000379:methods; D012984:Software; D018709:Statistics, Nonparametric