Attribute measurement policies for time and cost sensitive classification

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.