Abstract
This paper presents a novel application of fuzzy-rough set-based 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.
Work funded by the Daphne Jackson Trust and the Royal Academy of Engineering.
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© 2009 Springer-Verlag Berlin Heidelberg
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Shang, C., Barnes, D., Shen, Q. (2009). Taking Fuzzy-Rough Application to Mars. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_25
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DOI: https://doi.org/10.1007/978-3-642-10646-0_25
Publisher Name: Springer, Berlin, Heidelberg
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