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.
|Title of host publication||Rough Sets, Fuzzy Sets, Data Mining and Granular Computing|
|Number of pages||8|
|Publication status||Published - 2009|
|Event||12th International Conference, RSFDGrC 2009 - Delhi, India|
Duration: 15 Dec 2009 → 18 Dec 2009
|Conference||12th International Conference, RSFDGrC 2009|
|Period||15 Dec 2009 → 18 Dec 2009|