An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Machine Learning in Stepwise Diagnostic Process
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
An Application of Machine Learning in the Diagnosis of Ischaemic Heart Disease
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Quality assessment of individual classifications in machine learning and data mining
Knowledge and Information Systems
Incorporating confidence in a naive bayesian classifier
UM'05 Proceedings of the 10th international conference on User Modeling
Transductive reliability estimation for medical diagnosis
Artificial Intelligence in Medicine
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In the past decades Machine Learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not used in practice. One reason for this is that it is dificult to obtain an unbiased estimation of diagnose's reliability. We propose a general framework for reliability estimation, based on transductive inference. We show that our reliability estimation is closely connected with a general notion of significance tests. We compare our approach with classical stepwise diagnostic process where reliability of diagnose is presented as its post-test probability. The presented approach is evaluated in practice in the problem of clinical diagnosis of coronary artery disease, where significant improvements over existing techniques are achieved.