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

@article{Esmeir2008AnytimeIO,
  title={Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach},
  author={Saher Esmeir and Shaul Markovitch},
  journal={J. Artif. Intell. Res.},
  year={2008},
  volume={33},
  pages={1-31}
}
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… CONTINUE READING
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