ML Anthology

Reducing Misclassification Costs

Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal M. Ali, Timothy Hume, Clifford Brunk
ICML 1994 pp. 217-225
doi:10.1016/B978-1-55860-335-6.50034-9 /icml/1994/pazzani1994icml-reducing/

Abstract

We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification rules. The Reduced Cost Ordering algorithm, a new method for creating a decision list (i.e., an ordered set of rules) is described and compared to a variety of inductive learning approaches. Next, we describe approaches that attempt to minimize costs while avoiding overfitting, and introduce the Clause Prefix method for pruning decision lists. Finally, we consider reducing misclassification costs when a prior domain theory is available.

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Cite

Text

Pazzani et al. "Reducing Misclassification Costs." International Conference on Machine Learning, 1994. doi:10.1016/B978-1-55860-335-6.50034-9

Markdown

[Pazzani et al. "Reducing Misclassification Costs." International Conference on Machine Learning, 1994.](https://mlanthology.org/icml/1994/pazzani1994icml-reducing/) doi:10.1016/B978-1-55860-335-6.50034-9

BibTeX

@inproceedings{pazzani1994icml-reducing,
  title     = {{Reducing Misclassification Costs}},
  author    = {Pazzani, Michael J. and Merz, Christopher J. and Murphy, Patrick M. and Ali, Kamal M. and Hume, Timothy and Brunk, Clifford},
  booktitle = {International Conference on Machine Learning},
  year      = {1994},
  pages     = {217-225},
  doi       = {10.1016/B978-1-55860-335-6.50034-9},
  url       = {https://mlanthology.org/icml/1994/pazzani1994icml-reducing/}
}