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.
|Name||2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING|
|Conference||1st Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote|
|Period||26/08/09 → 29/08/09|
- Hyperspectral image classification; random subspace method; kernel smoothing