Anytime Algorithm for Feature Selection Export

@incollection{LAS00, abstract = {Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the quality of results by increasing the computation time. The algorithm is interruptible, i.e., it can be stopped at any time and provide a partial subset of selected features. The quality of results is monitored by a new measure: fuzzy information gain. The algorithm performance is evaluated on several benchmark datasets.}, author = {Last, Mark and Kandel, Abraham and Maimon, Oded and Eberbach, Eugene}, citeulike-article-id = {3347494}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-45554-X_66}, doi = {10.1007/3-540-45554-X_66}, journal = {Rough Sets and Current Trends in Computing}, keywords = {anytime, feature-selection, learning}, pages = {532--539}, posted-at = {2008-09-28 17:59:19}, priority = {0}, title = {Anytime Algorithm for Feature Selection}, url = {http://dx.doi.org/10.1007/3-540-45554-X_66}, year = {2001} }

TY - CHAP ID - LAS00 L3 - citeulike-article-id:3347494 N2 - Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the quality of results by increasing the computation time. The algorithm is interruptible, i.e., it can be stopped at any time and provide a partial subset of selected features. The quality of results is monitored by a new measure: fuzzy information gain. The algorithm performance is evaluated on several benchmark datasets. JF - Rough Sets and Current Trends in Computing EP - 539 TI - Anytime Algorithm for Feature Selection SP - 532 KW - anytime KW - feature-selection KW - learning AU - Last, Mark AU - Kandel, Abraham AU - Maimon, Oded AU - Eberbach, Eugene PY - 2001/// UR - http://dx.doi.org/10.1007/3-540-45554-X_66 ER -