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dc.contributor.author Shang, Changjing
dc.contributor.author Barnes, David Preston
dc.contributor.author Shen, Qiang
dc.date.accessioned 2013-07-23T13:40:51Z
dc.date.available 2013-07-23T13:40:51Z
dc.date.issued 2009
dc.identifier.citation Shang , C , Barnes , D P & Shen , Q 2009 , Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification . in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing . Springer Nature , pp. 209-216 , 12th International Conference, RSFDGrC 2009 , Delhi , India , 15 Dec 2009 . https://doi.org/10.1007/978-3-642-10646-0_25 en
dc.identifier.citation conference en
dc.identifier.isbn 978-3-642-10645-3
dc.identifier.isbn 978-3-642-10646-0
dc.identifier.other PURE: 222234
dc.identifier.other PURE UUID: 9036579e-c44a-43b9-83cb-999bbd084bdc
dc.identifier.other RAD: 692
dc.identifier.other DSpace_20121128.csv: row: 2564
dc.identifier.other Scopus: 76649119152
dc.identifier.other handle.net: 2160/11011
dc.identifier.uri http://hdl.handle.net/2160/11011
dc.description C. Shang, D. Barnes and Q. Shen. Taking fuzzy-rough application to Mars: Fuzzy-rough feature selection for Mars terrain image classification. Proceedings of the 12th International Conference on Rough Sets, LNAI 5908, pp. 209-216, 2009. Sponsorship: Daphne Jackson Trust and Royal Academy of Engineering en
dc.description.abstract This paper presents a novel application of fuzzy-rough setbased feature selection (FRFS) for Mars terrain image classification. The work allows the induction of low-dimensionality feature sets from sample descriptions of feature patterns of a much higher dimensionality. In particular, FRFS is applied in conjunction with multi-layer perceptron and K-nearest neighbor based classifiers. Supported with comparative studies, the paper demonstrates that FRFS helps to enhance the effectiveness and efficiency of conventional classification systems, 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 8 en
dc.language.iso eng
dc.publisher Springer Nature
dc.relation.ispartof Rough Sets, Fuzzy Sets, Data Mining and Granular Computing en
dc.rights en
dc.title Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference en
dc.description.version preprint en
dc.description.version preprint en
dc.identifier.doi https://doi.org/10.1007/978-3-642-10646-0_25
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Intelligent Robotics Group en


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