Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
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
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Incremental discretization for Naïve-Bayes classifier
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
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Naive-Bayes classifier is a popular technique of classification in machine learning. Improving the accuracy of naive-Bayes classifier will be significant as it has great importance in classification using numerical attributes. For numeric attributes, the conditional probabilities are either modeled by some continuous probability distribution over the range of that attribute's values or by conversion of numeric attribute to discrete one using discretization. The limitation of the classifier using discretization is that it does not classify those instances for which conditional probabilities of any of the attribute value for every class is zero. The proposed method resolves this limitation of estimating probabilities in the naive-Bayes classifier and improve the classification accuracy for noisy data. The proposed method is efficient and robust in estimating probabilities in the naive-Bayes classifier. The proposed method has been tested over a number of databases of UCI machine learning repository and the comparative results of existing naive-Bayes classifier and proposed method has also been illustrated.