The complexity of Boolean functions
The complexity of Boolean functions
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Information Retrieval
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Bayesian network modelling through qualitative patterns
Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exploiting functional dependence in bayesian network inference
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A new look at causal independence
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Knowledge engineering for large belief networks
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Parameter adjustment in Bayes networks. the generalized noisy OR-gate
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
EM algorithm for symmetric causal independence models
ECML'06 Proceedings of the 17th European conference on Machine Learning
Artificial Intelligence in Medicine
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
Ant colony based approach to predict stock market movement from mood collected on Twitter
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Artificial Intelligence in Medicine
Probabilistic inference with noisy-threshold models based on a CP tensor decomposition
International Journal of Approximate Reasoning
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Objective: To predict the development of carcinoid heart disease (CHD), which is a life-threatening complication of certain neuroendocrine tumors. To this end, a novel type of Bayesian classifier, known as the noisy-threshold classifier, is applied. Materials and methods: Fifty-four cases of patients that suffered from a low-grade midgut carcinoid tumor, of which 22 patients developed CHD, were obtained from the Netherlands Cancer Institute (NKI). Eleven attributes that are known at admission have been used to classify whether the patient develops CHD. Classification accuracy and area under the receiver operating characteristics (ROC) curve of the noisy-threshold classifier are compared with those of the naive-Bayes classifier, logistic regression, the decision-tree learning algorithm C4.5, and a decision rule, as formulated by an expert physician. Results: The noisy-threshold classifier showed the best classification accuracy of 72% correctly classified cases, although differences were significant only for logistic regression and C4.5. An area under the ROC curve of 0.66 was attained for the noisy-threshold classifier, and equaled that of the physician's decision-rule. Conclusions: The noisy-threshold classifier performed favorably to other state-of-the-art classification algorithms, and equally well as a decision-rule that was formulated by the physician. Furthermore, the semantics of the noisy-threshold classifier make it a useful machine learning technique in domains where multiple causes influence a common effect.