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2004

Decision Tree Induction for Dynamic, High-Dimensional Data Using P-Trees

11 years 3 months ago
Decision Tree Induction for Dynamic, High-Dimensional Data Using P-Trees
1 Decision Tree Induction is a powerful classification tool that is much used in practice and works well for static data with dozens of attributes. We adapt the decision tree concept to a setting where data changes rapidly and hundreds or thousands of attributes may be relevant. Decision tree branches are evaluated as needed, based on the most recent data, focusing entirely on the data that needs to be classified. Our algorithm is based on the P-tree data structure that allows fast evaluation of counts of data points, and results in scaling that is better than linear in the data set size.
Anne Denton, William Perrizo
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where CATA
Authors Anne Denton, William Perrizo
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