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Streaming feature selection using alpha-investing
Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05
In Streaming Feature Selection (SFS), new features are sequentially considered for addition to a predictive model. When the space of potential features is large, SFS offers many advantages over traditional feature selection methods, which assume that all features are known in advance. Features can be generated dynamically, focusing the search for new features on promising subspaces, and overfitting can be controlled by dynamically adjusting the threshold for adding features to the model. Wedoi:10.1145/1081870.1081914 dblp:conf/kdd/ZhouFSU05 fatcat:ah63pvlfijcbrjgybx43hukpae