Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Inference for the Generalization Error
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An overview of statistical learning theory
IEEE Transactions on Neural Networks
When in Doubt ... Be Indecisive
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Mining data with random forests: A survey and results of new tests
Pattern Recognition
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Scrapie is a neuro-degenerative disease in small ruminants. A data set of 3113 records of sheep reported to the Scrapie Notifications Database in Great Britain has been studied. Clinical signs were recorded as present/absent in each animal by veterinary officials (VO) and a post-mortem diagnosis was made. In an attempt to detect healthy animals within the set of suspects using only the clinical signs, 18 classification methods were applied ranging from simple linear classifiers to classifier ensembles such as Bagging, AdaBoost and Random Forests. The results suggest that the clinical classification by the VO was adequate as no further differentiation within the set of suspects was feasible.