%0 Journal Article %1 louw:2006 %A Louw, N. %A Steel, S. J. %C Amsterdam, The Netherlands, The Netherlands %D 2006 %I Elsevier Science Publishers B. V. %J Comput. Stat. Data Anal. %K discriminant discriminant-analysis fisher kernel variable-selection %N 3 %P 2043--2055 %R http://dx.doi.org/10.1016/j.csda.2005.12.018 %T Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination %U http://portal.acm.org/citation.cfm?id=1219314 %V 51 %X Variable selection serves a dual purpose in statistical classification problems: it enables one to identify the input variables which separate the groups well, and a classification rule based on these variables frequently has a lower error rate than the rule based on all the input variables. Kernel Fisher discriminant analysis (KFDA) is a recently proposed powerful classification procedure, frequently applied in cases characterised by large numbers of input variables. The important problem of eliminating redundant input variables before implementing KFDA is addressed in this paper. A backward elimination approach is recommended, and two criteria which can be used for recursive elimination of input variables are proposed and investigated. Their performance is evaluated on several data sets and in a simulation study.

@article{louw:2006, abstract = {Variable selection serves a dual purpose in statistical classification problems: it enables one to identify the input variables which separate the groups well, and a classification rule based on these variables frequently has a lower error rate than the rule based on all the input variables. Kernel Fisher discriminant analysis (KFDA) is a recently proposed powerful classification procedure, frequently applied in cases characterised by large numbers of input variables. The important problem of eliminating redundant input variables before implementing KFDA is addressed in this paper. A backward elimination approach is recommended, and two criteria which can be used for recursive elimination of input variables are proposed and investigated. Their performance is evaluated on several data sets and in a simulation study.}, added-at = {2010-02-15T12:14:29.000+0100}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Louw, N. and Steel, S. J.}, biburl = {https://www.bibsonomy.org/bibtex/2c897a5b1e5affa84e14900b484085dec/vivion}, description = {Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination}, doi = {http://dx.doi.org/10.1016/j.csda.2005.12.018}, interhash = {55c9a3ce72ab8476203b6fe5aeab1660}, intrahash = {c897a5b1e5affa84e14900b484085dec}, issn = {0167-9473}, journal = {Comput. Stat. Data Anal.}, keywords = {discriminant discriminant-analysis fisher kernel variable-selection}, number = 3, pages = {2043--2055}, publisher = {Elsevier Science Publishers B. V.}, timestamp = {2010-02-15T12:14:29.000+0100}, title = {Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimination}, url = {http://portal.acm.org/citation.cfm?id=1219314}, volume = 51, year = 2006 }