Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 22, Issue 6, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065712500268
Keywords
Feature ranking; distance measures; consensus methods; seabed texture classification
Categories
Funding
- EPSRC [R17336]
- UEA [R17336]
- Gardline Geosurvey of Great Yarmouth, UK [R17336]
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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.
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