Types of Cost in Inductive Concept Learning
<|reference_start|>Types of Cost in Inductive Concept Learning: Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth.<|reference_end|>
@article{turney2002types,
    title={Types of Cost in Inductive Concept Learning},
    author={Peter D. Turney (National Research Council of Canada)},
    journal={Workshop on Cost-Sensitive Learning at the Seventeenth
  International Conference on Machine Learning, (2000), Stanford University,
  California, 15-21},
    year={2002},
    number={NRC-43671},
    archivePrefix={arXiv},
    eprint={cs/0212034},
    primaryClass={cs.LG cs.CV}
}