期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2008
卷号：33
页码：1-31
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：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 datasets were
conducted to compare ACT to the state-of-the-art cost-sensitive tree learners.
The results show that for the majority of domains ACT produces significantly
less costly trees. ACT also exhibits good anytime behavior with diminishing
returns
