InterActive Feature Selection
IJCAI(2005)
摘要
We study the effects of feature selection and human
feedback on features in active learning settings.
Our experiments on a variety of text categorization
tasks indicate that there is significant potential
in improving classifier performance by feature
reweighting, beyond that achieved via selective
sampling alone (standard active learning) if we
have access to an oracle that can point to the important
(most predictive) features. Consistent with previous
findings, we find that feature selection based
on the labeled training set has little effect. But our
experiments on human subjects indicate that human
feedback on feature relevance can identify a sufficient
proportion (65%) of the most relevant features.
Furthermore, these experiments show that
feature labeling takes much less (about 1/5th) time
than document labeling. We propose an algorithm
that interleaves labeling features and documents
which significantly accelerates active learning.
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关键词
feature selection,relevant feature,interactive feature selection,feature reweighting,standard active learning,active learning,human subject,human feedback,active learning setting,classifier performance,feature relevance
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