Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach

Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassification costs. The increasing complexity of these applications poses a real challenge to resource management during learning and classification. In this work we introduce ACT (anytime cost-sensitive tree learner), a novel framework for operating in such complex environments. ACT is an anytime algorithm that allows learning time to be increased in return for lower classification costs. It builds a tree top-down and exploits additional time resources to obtain better estimations for the utility of the different candidate splits. Using sampling techniques, ACT approximates the cost of the subtree under each candidate split and favors the one with a minimal cost. As a stochastic algorithm, ACT is expected to be able to escape local minima, into which greedy methods may be trapped. Experiments with a variety of dataset...

• Type: Journal
• Year: 2008
• Where: JAIR
• Authors: Saher Esmeir, Shaul Markovitch
• Keywords: Complex Real-world Applications, Cost-sensitive Tree, Introduce Act, JAIR 2008