Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification

Changjing Shang, David Preston Barnes, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

1 Citation (SciVal)
204 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing
PublisherSpringer Nature
Pages209-216
Number of pages8
ISBN (Electronic)978-3-642-10646-0
ISBN (Print)978-3-642-10645-3
DOIs
Publication statusPublished - 2009
Event12th International Conference, RSFDGrC 2009 - Delhi, India
Duration: 15 Dec 200918 Dec 2009

Conference

Conference12th International Conference, RSFDGrC 2009
Country/TerritoryIndia
CityDelhi
Period15 Dec 200918 Dec 2009

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