DocumentCode
2331941
Title
Visualizing decision table classifiers
Author
Becker, Barry G.
Author_Institution
Silicon Graphics Inc., Mountain View, CA, USA
fYear
1998
fDate
19-20 Oct 1998
Firstpage
102
Abstract
Decision tables, like decision trees or neural nets, are classification models used for prediction. They are induced by machine learning algorithms. A decision table consists of a hierarchical table in which each entry in a higher level table gets broken down by the values of a pair of additional attributes to form another table. The structure is similar to dimensional stacking. A visualization method is presented that allows a model based on many attributes to be understood even by those unfamiliar with machine learning. Various forms of interaction are used to make this visualization more useful than other static designs
Keywords
data visualisation; decision tables; learning (artificial intelligence); pattern classification; attributes; classification models; decision table classifier visualization; dimensional stacking; hierarchical table; interaction; machine learning algorithms; prediction; Classification tree analysis; Data mining; Data visualization; Decision trees; Displays; Graphics; Layout; Predictive models; Stacking; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Visualization, 1998. Proceedings. IEEE Symposium on
Conference_Location
Research Triangle, CA
Print_ISBN
0-8186-9093-3
Type
conf
DOI
10.1109/INFVIS.1998.729565
Filename
729565
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