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CIDM
2009
IEEE

Large-scale attribute selection using wrappers

13 years 10 months ago
Large-scale attribute selection using wrappers
Abstract— Scheme-specific attribute selection with the wrapper and variants of forward selection is a popular attribute selection technique for classification that yields good results. However, it can run the risk of overfitting because of the extent of the search and the extensive use of internal cross-validation. Moreover, although wrapper evaluators tend to achieve superior accuracy compared to filters, they face a high computational cost. The problems of overfitting and high runtime occur in particular on high-dimensional datasets, like microarray data. We investigate Linear Forward Selection, a technique to reduce the number of attributes expansions in each forward selection step. Our experiments demonstrate that this approach is faster, finds smaller subsets and can even increase the accuracy compared to standard forward selection. We also investigate a variant that applies explicit subset size determination in forward selection to combat overfitting, where the search is...
Martin Gutlein, Eibe Frank, Mark Hall, Andreas Kar
Added 20 May 2010
Updated 20 May 2010
Type Conference
Year 2009
Where CIDM
Authors Martin Gutlein, Eibe Frank, Mark Hall, Andreas Karwath
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