Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Wavelet Feature Selection for Image Classification
IEEE Transactions on Image Processing
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
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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.