Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Data mining: concepts and techniques
Data mining: concepts and techniques
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)
A Parsimonious Constraint-based Algorithm to Induce Bayesian Network Structures from Data
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Expert Systems with Applications: An International Journal
Classification of Otoneurological Cases According to Bayesian Probabilistic Models
Journal of Medical Systems
Comparing risks of alternative medical diagnosis using Bayesian arguments
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Computers in Biology and Medicine
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We evaluate the effectiveness of seven Bayesian network classifiers as potential tools for the diagnosis of breast cancer using two real-world databases containing fine-needle aspiration of the breast lesion cases collected by a single observer and multiple observers, respectively. The results show a certain ingredient of subjectivity implicitly contained in these data: we get an average accuracy of 93.04% for the former and 83.31% for the latter. These findings suggest that observers see different things when looking at the samples in the microscope; a situation that significantly diminishes the performance of these classifiers in diagnosing such a disease.