Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
An entropy-based learning algorithm of Bayesian conditional trees
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
An algorithm for deciding if a set of observed independencies has a causal explanation
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Applying Bayesian networks to information retrieval
Communications of the ACM
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Learning in graphical models
Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Introduction to Bayesian Networks
Introduction to Bayesian Networks
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
treeNets: A Framework for Anytime Evaluation of Belief Networks
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Recognizing End-User Transactions in Performance Management
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Tractable Average-Case Analysis of Naive Bayesian Classifiers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Localized partial evaluation of belief networks
Localized partial evaluation of belief networks
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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
Induction of selective Bayesian networks from data
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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For reasoning under uncertainty the Bayesian Network has become the representation of choice. However, except were models are considered 'simple' the tasks of construction and inference are provably NP hard. For modelling larger real-world problems this computational complexity has been addressed by methods that approximate the model. The Naive Bayes (NB) Classifier which has strong assumptions of independence among features is a common approach whilst the class of trees another less extreme example. The aim of this paper is to investigate the use of an information theory based technique as a mechanism for inference in Singly Connected Networks (SCN) or 'polytrees'. We call this variant a Mutual Information Measure (MIM) Classifier. We experimentally evaluate this new approach and compare the resulting classification performance of the MIM Classifier against (a) a Naive Bayes Classifier, (b) a General Bayesian Network (GBN) Classifier and (c) a Singly Connected Network, using benchmark problems taken from the UCI repository. With respect to (a) we show that the MIM Classifier generally performs better than the NB Classifier. For (b) and (c) we show that the MIM Classifier is comparable with both the GBN and SCN Classifiers and in most data sets used performs marginally better.