Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Arbitrating among competing classifiers using learned referees
Knowledge and Information Systems
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Using Hard Classifiers to Estimate Conditional Class Probabilities
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Machine-Learning Applications of Algorithmic Randomness
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Characterizing Model Erros and Differences
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Transduction with Confidence and Credibility
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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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 being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose's reliability. We discuss how reliability of diagnoses is assessed in medical decision making and propose a general framework for reliability estimation in Machine Learning, based on transductive inference. We compare our approach with a usual (Machine Learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its posttest probability. The proposed transductive approach is evaluated on several medical data sets from the UCI (University of California, Irvine) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease. In all cases significant improvements over existing techniques are achieved.