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- Publisher
- Springer Journals
- Copyright
- Copyright © 2013 by Springer Science+Business Media Dordrecht
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Statistical Physics, Dynamical Systems and Complexity
- ISSN
- 1012-2443
- eISSN
- 1573-7470
- DOI
- 10.1007/s10472-013-9368-4
- Publisher site
- See Article on Publisher Site
Abstract
Inductive conformal predictors have been designed to overcome the computational inefficiency exhibited by conformal predictors for many underlying prediction algorithms. Whereas computationally efficient, inductive conformal predictors sacrifice different parts of the training set at different stages of prediction, which affects their informational efficiency. This paper introduces the method of cross-conformal prediction, which is a hybrid of the methods of inductive conformal prediction and cross-validation, and studies its validity and informational efficiency empirically. The computational efficiency of cross-conformal predictors is comparable to that of inductive conformal predictors, and they produce valid predictions in our empirical studies.
Journal
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Jul 10, 2013