Maximizing classifier utility when training data is costly

  title={Maximizing classifier utility when training data is costly},
  author={Gary M. Weiss and Ye Tian},
  journal={SIGKDD Explorations},
Classification is a well-studied problem in machine learning and data mining. Classifier performance was originally gauged almost exclusively using predictive accuracy. However, as work in the field progressed, more sophisticated measures of classifier utility that better represented the value of the induced knowledge were introduced. Nonetheless, most work still ignored the cost of acquiring training examples, even though this affects the overall utility of a classifier. In this paper we… CONTINUE READING


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