Wrappers for Feature Subset Selection
Ron Kohavi and George H. John

Wrappers for Feature Subset Selection.pdf 4.04MB
Type: Paper
Tags:

Bibtex:
@article{Kohavi:1997:WFS:270613.270627,
 author = {Ron Kohavi and George H. John},
 title = {Wrappers for Feature Subset Selection},
 journal = {Artificial Intelligence},
 issue_date = {Dec. 1997},
 volume = {97},
 number = {1-2},
 month = dec,
 year = {1997},
 issn = {0004-3702},
 pages = {273--324},
 numpages = {52},
 url = {},
 doi = {10.1016/S0004-3702(97)00043-X},
 keywords = {classification, feature seleciton, filter, wrapper},
	abstract = {In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular domain, a feature subset selection method should consider how the algorithm and the training data interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain. We study the strengths and weaknesses of the wrapper approach and show improvements over the original design. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter-based approach to feature subset selection. Significant improvement in accuracy on real problems is achieved for the two families of induction algorithms used: decision trees and Naive-Bayes.}
}

Send Feedback Start
   0.000005
DB Connect
   0.000472
Lookup hash in DB
   0.005098
Get torrent details
   0.001729
Get torrent details, finished
   0.000686
Get authors
   0.000006
Select authors
   0.000551
Parse bibtex
   0.000601
Write header
   0.000698
get stars
   0.000468
home tab
   0.000494
render right panel
   0.000032
render ads
   0.000094
fetch current hosters
   0.011314
Done