Wrappers for feature subset selection Export

@article{citeulike:530840, 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 training set, a feature subset selection method should consider how the algorithm and the training set 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 a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.}, address = {Essex, UK}, author = {Kohavi, R.}, citeulike-article-id = {530840}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=270627}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/S0004-3702(97)00043-X}, citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0004-3702(97)00043-X}, citeulike-linkout-3 = {http://www.sciencedirect.com/science/article/B6TYF-3SNYS10-9/2/da1f885e31a43d42e0d0fddcc922c407}, doi = {10.1016/S0004-3702(97)00043-X}, issn = {00043702}, journal = {Artificial Intelligence}, keywords = {feature-selection}, month = {December}, number = {1-2}, pages = {273--324}, posted-at = {2009-10-05 21:08:03}, priority = {2}, publisher = {Elsevier Science Publishers Ltd.}, title = {Wrappers for feature subset selection}, url = {http://dx.doi.org/10.1016/S0004-3702(97)00043-X}, volume = {97}, year = {1997} }

TY - JOUR ID - citeulike:530840 L3 - citeulike-article-id:530840 N2 - 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 training set, a feature subset selection method should consider how the algorithm and the training set 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 a series of improved designs. We compare the wrapper approach to induction without feature subset selection and to Relief, a filter approach to feature subset selection. Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes. IS - 1-2 JF - Artificial Intelligence SN - 00043702 AD - Essex, UK EP - 324 TI - Wrappers for feature subset selection PB - Elsevier Science Publishers Ltd. VL - 97 SP - 273 KW - feature-selection AU - Kohavi, R PY - 1997/12// UR - http://dx.doi.org/10.1016/S0004-3702(97)00043-X ER -