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

Changjing Shang; David Preston Barnes; Qiang Shen

doi:10.1007/978-3-642-10646-0_25

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

Changjing Shang, David Preston Barnes, Qiang Shen

Department of Computer Science

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

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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 language	English
Title of host publication	Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Publisher	Springer Nature
Pages	209-216
Number of pages	8
ISBN (Electronic)	978-3-642-10646-0
ISBN (Print)	978-3-642-10645-3
DOIs	https://doi.org/10.1007/978-3-642-10646-0_25
Publication status	Published - 2009
Event	12th International Conference, RSFDGrC 2009 - Delhi, India Duration: 15 Dec 2009 → 18 Dec 2009

Conference

Conference	12th International Conference, RSFDGrC 2009
Country/Territory	India
City	Delhi
Period	15 Dec 2009 → 18 Dec 2009

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http://hdl.handle.net/2160/3276

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title = "Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification",

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.",

author = "Changjing Shang and Barnes, {David Preston} and Qiang Shen",

note = "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; 12th International Conference, RSFDGrC 2009 ; Conference date: 15-12-2009 Through 18-12-2009",

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AU - Barnes, David Preston

AU - Shen, Qiang

N1 - 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

PY - 2009

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N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-10646-0_25

DO - 10.1007/978-3-642-10646-0_25

M3 - Conference Proceeding (Non-Journal item)

SN - 978-3-642-10645-3

SP - 209

EP - 216

BT - Rough Sets, Fuzzy Sets, Data Mining and Granular Computing

PB - Springer Nature

T2 - 12th International Conference, RSFDGrC 2009

Y2 - 15 December 2009 through 18 December 2009

ER -

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

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