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A new ensemble feature selection and its application to pattern classification
Received:October 15,2007  Revised:December 3,2008
Keywords:Rough sets reduction  Ensemble feature selection  Neural network ensemble  Remote sensing image classification
Fund Project:
Dongbo ZHANG, Yaonan WANG Institute of Information Engineering, Xiangtan University, Xiangtan Hunan 411105, China; College of Electrical and Information Engineering, Hunan University, Changsha Hunan 410082, China 
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      Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.
Dongbo ZHANG, Yaonan WANG.A new ensemble feature selection and its application to pattern classification[J].Journal of Control Theory and Applications,2009,7(4):419~426.
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