%0 Journal Article %1 keyhere %A Guo, Gongde %A Neagu, Daniel %A Cronin, Mark T.D. %D 2005 %J Pattern Recognition and Data Mining %K unread %P 410--419 %T Using kNN Model for Automatic Feature Selection %U http://dx.doi.org/10.1007/11551188_44 %X This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications – those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation based on inductive information in each representative obtained by kNN model. We have evaluated the performance of the proposed kNN model-based feature selection method on toxicity dataset Phenols with two different endpoints. Experimental results indicate that the proposed feature selection method has a significant improvement in the classification accuracy for the trial dataset. ER -

@article{keyhere, abstract = {This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications – those with both ordinal and nominal features; and (3) presenting a simple method of difference function calculation based on inductive information in each representative obtained by kNN model. We have evaluated the performance of the proposed kNN model-based feature selection method on toxicity dataset Phenols with two different endpoints. Experimental results indicate that the proposed feature selection method has a significant improvement in the classification accuracy for the trial dataset. ER -}, added-at = {2008-10-17T10:57:35.000+0200}, author = {Guo, Gongde and Neagu, Daniel and Cronin, Mark T.D.}, biburl = {https://www.bibsonomy.org/bibtex/29a43a1255896104679f67db7333593ac/rgolombe}, description = {SpringerLink - Book Chapter}, interhash = {47f9bf20813ed7a00e4ba91b9defa351}, intrahash = {9a43a1255896104679f67db7333593ac}, journal = {Pattern Recognition and Data Mining}, keywords = {unread}, pages = {410--419}, timestamp = {2008-10-17T10:57:35.000+0200}, title = {Using kNN Model for Automatic Feature Selection}, url = {http://dx.doi.org/10.1007/11551188_44}, year = 2005 }