Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Graph-Based model-selection framework for large ensembles
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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Feature selection for the individuals of bagging is studied in this paper. Ensemble learning like bagging can effectively improve the performance of single learning machines, and so can feature selection, but few has studied whether feature selection could improve bagging of single learning machines. Therefore, two typical feature selection approaches namely the embedded feature selection model with the prediction risk criteria and the filter model with the mutual information criteria are used for the bagging of support vector machines respectively. Experiments performed on the UCI data sets show the effectiveness of feature selection for the bagging of support vector machines.