An Inductive Learning Approach to Prognostic Prediction

Street, W. Nick; Mangasarian, Olvi L.; Wolberg, William H.

doi:10.1016/B978-1-55860-377-6.50071-2

An Inductive Learning Approach to Prognostic Prediction

W. Nick Street, Olvi L. Mangasarian, William H. Wolberg

ICML 1995 pp. 522-530

doi:10.1016/B978-1-55860-377-6.50071-2 /icml/1995/street1995icml-inductive/

Abstract

This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may be only a lower bound on the “right answer.” This approach is augmented with a feature selection method that chooses an appropriate feature set within the context of the linear programming generalizer. Computational results in the field of breast cancer prognosis are shown. A straightforward translation of the prediction method to an artificial neural network model is also proposed.

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Cite

Text

Street et al. "An Inductive Learning Approach to Prognostic Prediction." International Conference on Machine Learning, 1995. doi:10.1016/B978-1-55860-377-6.50071-2

Markdown

[Street et al. "An Inductive Learning Approach to Prognostic Prediction." International Conference on Machine Learning, 1995.](https://mlanthology.org/icml/1995/street1995icml-inductive/) doi:10.1016/B978-1-55860-377-6.50071-2

BibTeX

@inproceedings{street1995icml-inductive,
  title     = {{An Inductive Learning Approach to Prognostic Prediction}},
  author    = {Street, W. Nick and Mangasarian, Olvi L. and Wolberg, William H.},
  booktitle = {International Conference on Machine Learning},
  year      = {1995},
  pages     = {522-530},
  doi       = {10.1016/B978-1-55860-377-6.50071-2},
  url       = {https://mlanthology.org/icml/1995/street1995icml-inductive/}
}