SmartStripes - Looking under the Hood of Feature Subset Selection Methods

dc.contributor.authorMay, T.en_US
dc.contributor.authorDavey, J.en_US
dc.contributor.authorRuppert, T.en_US
dc.contributor.editorSilvia Miksch and Giuseppe Santuccien_US
dc.date.accessioned2014-01-27T15:55:54Z
dc.date.available2014-01-27T15:55:54Z
dc.date.issued2011en_US
dc.description.abstractWe propose a visualization method for the diagnosis and interactive refinement of automatic techniques for feature subset selection. So-called filter techniques use statistical ranking measures to identify the most useful combination of features for further analysis. Usually a measure is applied to all entities of a data-table. The influence of atypical entities can distort the result, but this distortion may be masked by the statistical aggregation. Clearly, feature and entity subset selection are highly interdependent. Our technique, SmartStripes, intends to make this interdependency visible.en_US
dc.description.seriesinformationEuroVA 2011: International Workshop on Visual Analyticsen_US
dc.identifier.isbn978-3-905673-82-1en_US
dc.identifier.urihttps://doi.org/10.2312/PE/EuroVAST/EuroVA11/013-016en_US
dc.publisherThe Eurographics Associationen_US
dc.titleSmartStripes - Looking under the Hood of Feature Subset Selection Methodsen_US
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