An experimental study of several decision issues for feature selection with multi-layer perceptrons
作者： E. Romero , J.M. Sopena
DOI: 10.1109/IJCNN.2005.1556181
关键词:
摘要: An experimental study of several decision issues for wrapper feature selection with multi-layer perceptrons is presented, namely the stopping criterion, data set where saliency measured and network retraining before computing saliency. Experimental results sequential backward procedure indicate that increase in computational cost associated every temporarily removed rewarded a significant performance improvement. Despite being quite intuitive, this idea has been hardly used practice. Regarding criterion measured, profits from measuring validation set, as reasonably expected. A somehow non-intuitive conclusion can be drawn by looking at it suggested forcing overtraining may useful early stopping.
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