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
C4.5: programs for machine learning
C4.5: programs for machine learning
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
Machine Learning - Special issue on learning with probabilistic representations
A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Task-based information management
ACM Computing Surveys (CSUR)
Lazy Learning of Bayesian Rules
Machine Learning
Visualizing high-dimensional predicitive model quality
Proceedings of the conference on Visualization '00
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Machine Learning - Special issue: Unsupervised learning
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
On predictive distributions and Bayesian networks
Statistics and Computing
Data Categorization Using Decision Trellises
IEEE Transactions on Knowledge and Data Engineering
Candidate Elimination Criteria for Lazy Bayesian Rules
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Hierarchical Classification of Documents with Error Control
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Floating search algorithm for structure learning of Bayesian network classifiers
Pattern Recognition Letters
Learning probabilistic networks
The Knowledge Engineering Review
Integrating wireless EEGs into medical sensor networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
On the use of Bayesian Networks to develop behaviours for mobile robots
Robotics and Autonomous Systems
Integrating Naïve Bayes and FOIL
The Journal of Machine Learning Research
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
When in Doubt ... Be Indecisive
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Classification of MedLine Documents Using MeSH Terms
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Integrating learning from examples into the search for diagnostic policies
Journal of Artificial Intelligence Research
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Artificial Intelligence Review
AEE'06 Proceedings of the 5th WSEAS international conference on Applications of electrical engineering
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
FADS: a fuzzy anomaly detection system
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Learning TAN from incomplete data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A bayesian metric for evaluating machine learning algorithms
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Enhancing SNNB with local accuracy estimation and ensemble techniques
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Developing cognitive models for social simulation from survey data
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Job performance prediction in a call center using a naive Bayes classifier
Expert Systems with Applications: An International Journal
Graphical models as surrogates for complex ground motion models
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state of the art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we examine and evaluate approaches for inducing classifiers from data, based on recent results in the theory of learning Bayesian networks. Bayesian networks are factored representations of probability distributions that generalize the naive Bayes classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness which are characteristic of naive Bayes. We experimentally tested these approaches using benchmark problems from the U. C. Irvine repository, and compared them against C4.5, naive Bayes, and wrapper-based feature selection methods.