Pruning Improves Heuristic Search for Cost-Sensitive Learning
This paper addresses cost sensitive classi cation in the setting where there are costs for measuring each attribute as well as costs for misclassi cation errors We show how to formulate this as a Markov Decision Pro cess in which the transition model is learned from the training data Speci cally we as sume a set of training examples in which all attributes and the true class have been measured We describe a learning algorithm based on the AO heuristic search procedure that searches for the classi cation policy with minimum expected cost We provide an ad missible heuristic for AO that substantially reduces the number of nodes that need to be expanded particularly when attribute mea surement costs are high To further prune the search space we introduce a statistical prun ing heuristic based on the principle that if the values of two policies are statistically in distinguishable on the training data then we can prune one of the policies from the AO search space Experiments with realis tic and synthetic data demonstrate that these heuristics can substantially reduce the mem ory needed for AO search without signi cantly a ecting the quality of the learned pol icy Hence these heuristics expand the range of cost sensitive learning problems for which AO is feasible