Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Algorithms for Feature Selection: An Evaluation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Bayesian network classifiers versus k-NN classifier using sequential feature selection
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Bayesian network classifiers versus selective k-NN classifier
Pattern Recognition
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
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This paper presents a floating search approach for learning the network structure of Bayesian network classifiers. A Bayesian network classifier is used which in combination with the search algorithm allows simultaneous feature selection and determination of the structure of the classifier.The introduced search algorithm enables conditional exclusions of previously added attributes and/or arcs from the network classifier. Hence, this algorithm is able to correct the network structure by removing attributes and/or arcs between the nodes if they become superfluous at a later stage of the search. Classification results of selective unrestricted Bayesian network classifiers are compared to naïve Bayes classifiers and tree augmented naïve Bayes classifiers. Experiments on different data sets show that selective unrestricted Bayesian network classifiers achieve a better classification accuracy estimate in two domains compared to tree augmented naïve Bayes classifiers, whereby in the remaining domains the performance is similar. However, the achieved network structure of selective unrestricted Bayesian network classifiers is simpler.