Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
Information-Based Evaluation Criterion for Classifier's Performance
Machine Learning
Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
On estimating probabilities in tree pruning
EWSL-91 Proceedings of the European working session on learning on Machine learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Readings in Machine Learning
Machine Learning
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Applications of machine learning: matching problems to tasks and methods
The Knowledge Engineering Review
Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis
Expert Systems with Applications: An International Journal
An intelligent model for liver disease diagnosis
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
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We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability.