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
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Inference for the Generalization Error
Machine Learning
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
When discriminative learning of Bayesian network parameters is easy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Lazy propagation in junction trees
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Latent variable discovery in classification models
Artificial Intelligence in Medicine
Hierarchical naive bayes models for representing user profiles
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators
ECML '07 Proceedings of the 18th European conference on Machine Learning
Classification Using Improved Hybrid Wavelet Neural Networks
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
HODE: Hidden One-Dependence Estimator
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Confusion and Distance Metrics as Performance Criteria for Hierarchical Classification Spaces
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A hierarchical Naïve Bayes model for approximate identity matching
Decision Support Systems
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Temporal rating habits: a valuable tool for rating discrimination
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
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Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well-performing set of classifiers is the Naïve Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to "information double-counting" and interaction omission.In this paper we focus on a relatively new set of models, termed Hierarchical Naïve Bayes models. Hierarchical Naïve Bayes models extend the modeling flexibility of Naïve Bayes models by introducing latent variables to relax some of the independence statements in these models. We propose a simple algorithm for learning Hierarchical Naïve Bayes models in the context of classification. Experimental results show that the learned models can significantly improve classification accuracy as compared to other frameworks.