A Hybrid Feature Selection Approach Based on the Bayesian Network Classifier and Rough Sets
The paper proposes a hybrid feature selection approach based on Rough sets and Bayesian network classifiers.In the approach,the classification result of a Bayesian network is used as the criterion for the optimal feature subset selection.The Bayesian network classifier used in the paper is a kind of naive Bayesian classifier.It is employed to implement classification by learning the samples consisting of a set of texture features.In order to simplify feature reduction using Rough Sets,a discrete method based on C-means clustering method is also presented.The proposed approach is applied to extract residential areas from panchromatic SPOT5 images.Experiment results show that the proposed method not only improves classification quality but also reduces computational cost.
Rough Sets Feature Selection Naive Bayesian Network Classifier
Li Pan Hong Zheng Li Li
School of Remote Sensing and Information Engineering Wuhan University 129 Luoyu Road,Wuhan,Hubei 430 School of Electronic Information Wuhan University 129 Luoyu Road,Wuhan,Hubei 430079,P.R.China
国际会议
成都
英文
707-714
2008-05-17（万方平台首次上网日期，不代表论文的发表时间）