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
Principles of data mining
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Quality assessment of individual classifications in machine learning and data mining
Knowledge and Information Systems
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
Single-stacking conformity approach to reliable classification
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
k-version-space multi-class classification based on k-consistency tests
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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Estimating the reliability of individual classifications is very important in several applications such as medical diagnosis. Recently, the transductive approach to reliability estimation has been proved to be very efficient when used with several machine learning classifiers, such as Naive Bayes and decision trees. However, the efficiency of the transductive approach for state-of-the art kernel-based classifiers was not considered. In this work we deal with this problem and apply the transductive reliability methodology with sparse kernel classifiers, specifically the Support Vector Machine and Relevance Vector Machine. Experiments with medical and bioinformatics datasets demonstrate better performance of the transductive approach for reliability estimation compared to reliability measures obtained directly from the output of the classifiers. Furthermore, we apply the methodology in the problem of reliable diagnostics of the coronary artery disease, outperforming the expert physicians' standard approach.