Saher Esmeir, Shaul Markovitch
Machine learning techniques are increasingly being used to produce a wide-range of classiﬁers for complex real-world applications that involve nonuniform testing costs and misclassiﬁcation costs. As the complexity of these applications grows, the management of resources during the learning and classiﬁcation processes be- comes a challenging task. In this work we introduce ACT (Anytime Cost-sensitive Trees), a novel framework for operating in such environments. ACT is an anytime algorithm that allows trading computation time for lower classiﬁcation costs. It builds a tree top-down and exploits additional time resources to obtain better esti- mations for the utility of the different candidate splits. Using sampling techniques ACT approximates for each candidate split the cost of the subtree under it and fa- vors the one with a minimal cost. Due to its stochastic nature ACT is expected to be able to escape local minima, into which greedy methods may be trapped. Ex- periments with a variety of datasets were conducted to compare the performance of ACT to that of the state of the art cost-sensitive tree learners. The results show that for most domains ACT produces trees of signiﬁcantly lower costs. ACT is also shown to exhibit good anytime behavior with diminishing returns.