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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46506

Title: Fuzzy Fusion Method for Combining Small Number of Classifiers in Hyperspectral Image Classification
Authors: Chun-Hsiang Chuang
Bor-Chen Kuo
Hsuan-Po Wang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Hyperspectral imaging;Image classification;Voting;Intelligent systems;Fuzzy systems;Statistics;Machinery;Displays;Kernel;Smoothing methods
Date: 2008-11
Issue Date: 2018-05-21T07:26:26Z
Publisher: Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Abstract: Abstract:
For hyperspectral image classification problem, the random subspace method has been shown that is a good approach to overcome the small sample problem, and the machinery of it is to randomly select a batch of subspaces to train different classifiers and then get the final decision by using the majority vote method. Theoretically, more classifiers we train, more stable and more accurate result we obtain. However, it shows the bad outcome when using small number of classifiers. In this paper, a fuzzy measure has been applied into the fusion process as a new evaluation to combine classifiers to try to improve the performance in the situation of less classifier. From the experiment results, it displays that this fuzzy measure has effectively progressed in the classification accuracy.
Relation: pp.327-332
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46506
Appears in Collections:[資訊工程學系] 演講及研討會

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