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Guiding Feature Subset Selection with an Interactive Visualization

May, Thorsten and Bannach, Andreas and Davey, James and Ruppert, Tobias and Kohlhammer, Jörn (2011):
Guiding Feature Subset Selection with an Interactive Visualization.
IEEE Press, New York, In: IEEE Conference on Visual Analytics Science and Technology 2011. Proceedings, DOI: 10.1109/VAST.2011.6102448, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2011
Creators: May, Thorsten and Bannach, Andreas and Davey, James and Ruppert, Tobias and Kohlhammer, Jörn
Title: Guiding Feature Subset Selection with an Interactive Visualization
Language: English
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.

Publisher: IEEE Press, New York
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Generalized digital documents, Feature selection, Visual analytics, Multidimensional data visualization, Visualization of multidimensional feature spaces, Mixed initiative
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: IEEE Conference on Visual Analytics Science and Technology 2011. Proceedings
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1109/VAST.2011.6102448
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