SUBSPACE SELECTION BASED MULTIPLE CLASSIFIER SYSTEMS FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Bor-Chen Kuo, Chun-Hsiang Chuang, Cheng-Hsuan Li, Chin-Teng Lin

研究成果: Conference contribution同行評審

3 引文 斯高帕斯(Scopus)

摘要

In a typical supervised classification task, the size of training data fundamentally affects the generality of a classifier. Given a finite and fixed size of training data, the classification result may be degraded as the number of features (dimensionality) increase. Many researches have demonstrated that multiple classifier systems (MCS) or so-called ensembles can alleviate small sample size and high dimensionality concern, and obtain more outstanding and robust results than single models. One of the effective approaches for generating an ensemble of diverse base classifiers is the use of different feature subsets such as random subspace method (RSM). The objective of this research is to develop a novel ensemble technique based on cluster algorithms for strengthening RSM. The results of real data experiments show that the proposed method obtains the sound performance especially in the situation of using less number of classifiers.
原文English
主出版物標題2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING
發行者IEEE
頁面211
ISBN(列印)978-1-4244-4686-5
出版狀態Published - 2009
事件1st Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote - Grenoble, France
持續時間: 26 八月 200929 八月 2009

出版系列

名字2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING
發行者IEEE

Conference

Conference1st Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote
國家France
城市 Grenoble
期間26/08/0929/08/09

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