Name
Title | ||
|---|---|---|
A hybrid feature selection approach based on the Bayesian network classifier and rough sets |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-79721-0_94 | RSKT |
Keywords | Field | DocType |
feature reduction,bayesian network,rough set,hybrid feature selection approach,c-means clustering method,naive bayesian classifier,optimal feature subset selection,bayesian network classifier,discrete method,classification result,classification quality,feature selection,rough sets | Variable-order Bayesian network,Feature selection,Pattern recognition,Panchromatic film,Computer science,Bayesian network classifier,Rough set,Bayesian network,Artificial intelligence,Cluster analysis,Machine learning,Naive bayesian classifier | Conference |
Volume | ISSN | ISBN |
5009 | 0302-9743 | 3-540-79720-3 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Pan | 1 | 20 | 3.13 |
| Hong Zheng | 2 | 14 | 3.29 |
| Li Li | 3 | 0 | 0.34 |