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
Machine Learning in Stepwise Diagnostic Process
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Making Reliable Diagnoses with Machine Learning: A Case Study
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
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
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Local sparsity control for naive Bayes with extreme misclassification costs
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Internet as a knowledge base for medical diagnostic assistance
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Microprocessors & Microsystems
Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
DMCS: Dual-Model Classification System and Its Application in Medicine
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Computers in Biology and Medicine
ASIC Design of a Digital Fuzzy System on Chip for Medical Diagnostic Applications
Journal of Medical Systems
Improved naive bayes for extremely skewed misclassification costs
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
TTLSC – transductive total least square model for classification and its application in medicine
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Ranking and selection of unsupervised learning marketing segmentation
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In the past decades, machine learning (ML) 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 post-test probability. The proposed transductive approach is evaluated on several medical datasets from the University of California (UCI) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease (CAD). In all cases, significant improvements over existing techniques are achieved.