Feature Selection for High-Dimensional Data with RapidMiner

dc.contributor.author	Sangkyun, Lee
dc.contributor.author	Schowe, Benjamin
dc.contributor.author	Sivakumar, Viswanath
dc.contributor.author	Morik, Katharina
dc.date.accessioned	2018-10-12T12:25:02Z
dc.date.available	2018-10-12T12:25:02Z
dc.date.issued	2011-01
dc.description.abstract	Feature selection is an important task in machine learning, reducing dimensionality of learning problems by selecting few relevant features without losing too much information. Focusing on smaller sets of features, we can learn simpler models from data that are easier to understand and to apply. In fact, simpler models are more robust to input noise and outliers, often leading to better prediction performance than the models trained in higher dimensions with all features. We implement several feature selection algorithms in an extension of RapidMiner, that scale well with the number of features compared to the existing feature selection operators in RapidMiner.	en
dc.identifier.uri	http://hdl.handle.net/2003/37189
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-19185
dc.language.iso	en	de
dc.relation.ispartofseries	Technical report / Sonderforschungsbereich Verfügbarkeit von Information durch Analyse unter Ressourcenbeschränkung;1/2011
dc.subject.ddc	004
dc.title	Feature Selection for High-Dimensional Data with RapidMiner	en
dc.type	Text	de
dc.type.publicationtype	report	de
dcterms.accessRights	open access
eldorado.secondarypublication	false	de

Files

Now showing 1 - 1 of 1

Now showing 1 - 1 of 1