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An Inductive Learning Approach to Prognostic Prediction (1995)
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| Venue: | Proceedings of the Twelfth International Conference on Machine Learning |
| Citations: | 22 - 4 self |
BibTeX
@INPROCEEDINGS{Street95aninductive,
author = {Nick Street and O. L. Mangasarian and W. H. Wolberg},
title = {An Inductive Learning Approach to Prognostic Prediction},
booktitle = {Proceedings of the Twelfth International Conference on Machine Learning},
year = {1995},
pages = {522--530},
publisher = {Morgan Kaufmann}
}
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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. 1 INTRODUCTION Machine learning methods have been successfully applied to the analysis of many different complex problems in recent years, including many biomedical applications. One field which can benefit from this type of approach is the analysis of survival or lifetime data (Lee, 1992; Miller Jr., 1981), in which the objective can be broadly defined as predicting...
Keyphrases
inductive learning approach prognostic prediction many biomedical application censored training example artificial neural network model recurrence surface approximation prediction method introduction machine many different complex problem recurrence time computational result linear programming appropriate feature feature selection method available training output miller jr linear programming generalizer recent year inductive learning method lifetime data breast cancer prognosis straightforward translation
