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Fourth IEEE International Conference on Data Mining (ICDM'04) (2004)
Brighton, United Kingdom
Nov. 1, 2004 to Nov. 4, 2004
ISBN: 0-7695-2142-8
pp: 323-326
Andrew Arnt , University of Massachusetts at Amherst
Shlomo Zilberstein , University of Massachusetts at Amherst
ABSTRACT
Attribute measurement is an important component of classification algorithms, which could limit their applicability in realtime settings. The time taken to assign a value to an unknown attribute may reduce the overall utility of the final result. We identify three different costs that must be considered, including a time sensitive utility function. We model this attribute measurement problem as a Markov decision process (MDP), and build a policy to control this process using AO* heuristic search. The results offer a cost-effective approach to attribute measurement and classification for a variety of realtime applications.
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CITATION

A. Arnt and S. Zilberstein, "Attribute Measurement Policies for Time and Cost Sensitive Classification," Fourth IEEE International Conference on Data Mining (ICDM'04)(ICDM), Brighton, United Kingdom, 2004, pp. 323-326.
doi:10.1109/ICDM.2004.10051
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