Machine Learning - Special issue on learning with probabilistic representations
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Lazy Learning of Bayesian Rules
Machine Learning
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Weighted Naive Bayes with Accurate Ranking
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
TAN Classifiers Based on Decomposable Distributions
Machine Learning
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A decision tree-based attribute weighting filter for naive Bayes
Knowledge-Based Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
The Journal of Machine Learning Research
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
The Journal of Machine Learning Research
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Robust bayesian linear classifier ensembles
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in other machine learning algorithms, use weighting to place more emphasis on highly predictive attributes than those that are less predictive. In this paper, we argue that for naive Bayes attribute weighting should instead be used to alleviate the conditional independence assumption. Based on this premise, we propose a weighted naive Bayes algorithm, called WANBIA, that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions. We perform extensive evaluations and find that WANBIA is a competitive alternative to state of the art classifiers like Random Forest, Logistic Regression and A1DE.