Effective Feature Selection for Mars McMurdo Terrain Image Classification


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dc.contributor.author Barnes, David Preston
dc.contributor.author Shang, Changjing
dc.contributor.author Shen, Qiang
dc.date.accessioned 2009-12-07T08:42:11Z
dc.date.available 2009-12-07T08:42:11Z
dc.date.issued 2009
dc.identifier.citation Barnes , D P , Shang , C & Shen , Q 2009 , ' Effective Feature Selection for Mars McMurdo Terrain Image Classification ' pp. 1419-1424 . en
dc.identifier.other PURE: 142340
dc.identifier.other dspace: 2160/3776
dc.identifier.uri http://hdl.handle.net/2160/3776
dc.description C. Shang, D. Barnes and Q. Shen. Effective feature selection for Mars McMurdo image classification. Proceedings of the 9th International Conference on Intelligent Systems Design and Applications, pp. 1419-1424, 2009. Sponsorship: Daphne Jackson Trust and Royal Academy of Engineering en
dc.description.abstract This paper presents a novel study of the classification of large-scale Mars McMurdo panorama image. Three dimensionality reduction techniques, based on fuzzy-rough sets, information gain ranking, and principal component analysis respectively, are each applied to this complicated image data set to support learning effective classifiers. The work allows the induction of low-dimensional feature subsets from feature patterns of a much higher dimensionality. To facilitate comparative investigations, two types of image classifier are employed here, namely multi-layer perceptrons and K-nearest neighbors. Experimental results demonstrate that feature selection helps to increase the classification efficiency by requiring considerably less features, while improving the classification accuracy by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Effective Feature Selection for Mars McMurdo Terrain Image Classification en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en

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