Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Information Sciences: an International Journal
Connectionist learning of belief networks
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Asymmetric parallel Boltzmann machines are belief networks
Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Combining Symbolic and Neural Learning
Machine Learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Statistical versus Relational Join Dependencies
Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management
Data Mining: the search for knowledge in databases.
Data Mining: the search for knowledge in databases.
Probabilistic Independence Networks for Hidden Markov Probability Models
Probabilistic Independence Networks for Hidden Markov Probability Models
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
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We introduce a probabilistic graphical model for supervised learning on databases with categorical attributes. The proposed belief network contains hidden variables that play a role similar to nodes in decision trees and each of their states either corresponds to a class label or to a single attribute test. As a major difference with respect to decision trees, the selection of the attribute to be tested is probabilistic. Thus, the model can be used to assess the probability that a tuple belongs to some class, given the predictive attributes. Unfolding the network along the hidden states dimension yields a trellis structure having a signal flow similar to second order connectionist networks. The network encodes context specific probabilistic independencies to reduce parametric complexity. We present a custom tailored inference algorithm and derive a learning procedure based on the expectation-maximization algorithm. We propose decision trellises as an alternative to decision trees in the context of tuple categorization in databases, which is an important step for building data mining systems. Preliminary experiments on standard machine learning databases are reported, comparing the classification accuracy of decision trellises and decision trees induced by C4.5. In particular, we show that the proposed model can offer significant advantages for sparse databases in which many predictive attributes are missing.