C2FS: An Algorithm for Feature Selection in Cascade Neural Networks
IJCNN(2006)
摘要
Wrapper-based feature selection is attractive be- cause wrapper methods are able to optimize the fea- tures they select to the specific learning algorithm. Unfortunately, wrapper methods are prohibitively expensive to use with neural nets. We present an internal wrapper feature selection method for Cas- cade Correlation (C2) nets called C2FS that is 2- 3 orders of magnitude faster than external wrapper feature selection. This new internal wrapper feature selection method selects features at the same time hidden units are being added to the growing C2 net architecture. Experiments with five test problems show that C2FS feature selection usually improves accuracy and squared error while dramatically re- ducing the number of features needed for good per- formance. Comparison to feature selection via an information theoretic ordering on features (gain ra- tio) shows that C2FS usually yields better perfor- mance and always uses substantially fewer features.
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关键词
neural network,neural net,feature selection,workstations,intelligent networks,computer science,concurrent computing,neural networks,testing,artificial neural networks
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