Theoretical and Empirical Analysis of ReliefF and RReliefF Export

@article{citeulike:2801576, abstract = {Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming.}, author = {Robnik-\v{S}ikonja, Marko and Kononenko, Igor}, citeulike-article-id = {2801576}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1025667309714}, day = {1}, doi = {10.1023/A:1025667309714}, journal = {Machine Learning}, month = {October}, number = {1}, pages = {23--69}, posted-at = {2009-08-30 14:53:32}, priority = {2}, title = {Theoretical and Empirical Analysis of ReliefF and RReliefF}, url = {http://dx.doi.org/10.1023/A:1025667309714}, volume = {53}, year = {2003} }

TY - JOUR ID - citeulike:2801576 L3 - citeulike-article-id:2801576 N2 - Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming. IS - 1 JF - Machine Learning EP - 69 TI - Theoretical and Empirical Analysis of ReliefF and RReliefF VL - 53 SP - 23 AU - Robnik-Šikonja, Marko AU - Kononenko, Igor PY - 2003/10/01/ UR - http://dx.doi.org/10.1023/A:1025667309714 ER -