Wrappers for Feature Subset Selection Export

@article{citeulike:1146004, 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...}, author = {Kohavi, Ron and John, George H.}, citeulike-article-id = {1146004}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.720}, journal = {Artificial Intelligence}, keywords = {feature-selection, learning}, number = {1-2}, pages = {273--324}, posted-at = {2008-03-22 11:39:21}, priority = {4}, title = {Wrappers for Feature Subset Selection}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.720}, volume = {97}, year = {1997} }

TY - JOUR ID - citeulike:1146004 L3 - citeulike-article-id:1146004 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... IS - 1-2 JF - Artificial Intelligence EP - 324 TI - Wrappers for Feature Subset Selection VL - 97 SP - 273 KW - feature-selection KW - learning AU - Kohavi, Ron AU - John, George PY - 1997/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.720 ER -