Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Enhancements to the data mining process
Enhancements to the data mining process
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Machine Learning
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Semi-Naive Bayesian Classifier
EWSL '91 Proceedings of the European Working Session on Machine Learning
Induction of Recursive Bayesian Classifiers
ECML '93 Proceedings of the European Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Adjusted Probability Naive Bayesian Induction
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
Tractable Average-Case Analysis of Naive Bayesian Classifiers
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Correlation Integral Decomposition for Classification
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Adaptive Bayesian network classifiers
Intelligent Data Analysis
Rough Ensemble Classifier: A Comparative Study
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Fixing weakly annotated web data using relational models
ICWE'07 Proceedings of the 7th international conference on Web engineering
Iterative reordering of rules for building ensembles without relearning
DS'07 Proceedings of the 10th international conference on Discovery science
Ensembles of jittered association rule classifiers
Data Mining and Knowledge Discovery
NB+: An improved Naïve Bayesian algorithm
Knowledge-Based Systems
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability class distribution associated with each training example. In this paper we argue that Iterative Bayes minimizes a quadratic loss function instead of the 0-1 loss function that usually applies to classification problems. Experimental evaluation of Iterative Bayes on 27 benchmark data sets shows consistent gains in accuracy. An interesting side effect of our algorithm is that it shows to be robust to attribute dependencies.