Communications of the ACM - Special issue on parallelism
A Nearest Hyperrectangle Learning Method
Machine Learning
Trading MIPS and memory for knowledge engineering
Communications of the ACM
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Lazy Learning of Bayesian Rules
Machine Learning
Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods
ECML '93 Proceedings of the European Conference on Machine Learning
A Practical Approach to Feature Selection
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth 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
Examining Locally Varying Weights for Nearest Neighbor Algorithms
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Data Mining with Products of Trees
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Learning Weighted Naive Bayes with Accurate Ranking
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Poisson naive Bayes for text classification with feature weighting
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Analysis of Naive Bayes' assumptions on software fault data: An empirical study
Data & Knowledge Engineering
Improve the Accuracy of One Dependence Augmented Naive Bayes by Weighted Attribute
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
Feature interval learning algorithms for classification
Knowledge-Based Systems
Partition-conditional ICA for Bayesian classification of microarray data
Expert Systems with Applications: An International Journal
Data classification using rough sets and naïve Bayes
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
A 'non-parametric' version of the naive Bayes classifier
Knowledge-Based Systems
Intelligent Naïve Bayes-based approaches for Web proxy caching
Knowledge-Based Systems
Not so greedy: Randomly Selected Naive Bayes
Expert Systems with Applications: An International Journal
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
Automated feature weighting in naive bayes for high-dimensional data classification
Proceedings of the 21st ACM international conference on Information and knowledge management
Boosting for superparent-one-dependence estimators
International Journal of Computing Science and Mathematics
Alleviating naive Bayes attribute independence assumption by attribute weighting
The Journal of Machine Learning Research
Learning attribute weighted AODE for ROC area ranking
International Journal of Information and Communication Technology
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The naive Bayes classifier continues to be a popular learning algorithm for data mining applications due to its simplicity and linear run-time. Many enhancements to the basic algorithm have been proposed to help mitigate its primary weakness - the assumption that attributes are independent given the class. All of them improve the performance of naive Bayes at the expense (to a greater or lesser degree) of execution time and/or simplicity of the final model. In this paper we present a simple filter method for setting attribute weights for use with naive Bayes. Experimental results show that naive Bayes with attribute weights rarely degrades the quality of the model compared to standard naive Bayes and, in many cases, improves it dramatically. The main advantages of this method compared to other approaches for improving naive Bayes is its run-time complexity and the fact that it maintains the simplicity of the final model.