May, Thorsten ; Bannach, Andreas ; Davey, James ; Ruppert, Tobias ; Kohlhammer, Jörn (2011)
Guiding Feature Subset Selection with an Interactive Visualization.
IEEE Conference on Visual Analytics Science and Technology 2011. Proceedings.
doi: 10.1109/VAST.2011.6102448
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2011 |
| Autor(en): | May, Thorsten ; Bannach, Andreas ; Davey, James ; Ruppert, Tobias ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Guiding Feature Subset Selection with an Interactive Visualization |
| Sprache: | Englisch |
| Publikationsjahr: | 2011 |
| Verlag: | IEEE Press, New York |
| Veranstaltungstitel: | IEEE Conference on Visual Analytics Science and Technology 2011. Proceedings |
| DOI: | 10.1109/VAST.2011.6102448 |
| Kurzbeschreibung (Abstract): | We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features. |
| Freie Schlagworte: | Business Field: Visual decision support, Research Area: Generalized digital documents, Feature selection, Visual analytics, Multidimensional data visualization, Visualization of multidimensional feature spaces, Mixed initiative |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 12 Nov 2018 11:16 |
| Letzte Änderung: | 12 Nov 2018 11:16 |
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