Machine Learning - Special issue on learning with probabilistic representations
Machine Learning
Classifier Learning with Supervised Marginal Likelihood
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
Partition-conditional ICA for Bayesian classification of microarray data
Expert Systems with Applications: An International Journal
A meta-heuristic approach for improving the accuracy in some classification algorithms
Computers and Operations Research
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
A 'non-parametric' version of the naive Bayes classifier
Knowledge-Based Systems
Learning feature-projection based classifiers
Expert Systems with Applications: An International Journal
Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Naive Bayes induction algorithm is very popular in classification field. Traditional method for dealing with numeric data is to discrete numeric attributes data into symbols. The difference of distinct discredited criteria has significant effect on performance. Moreover, several researches had recently employed the normal distribution to handle numeric data, but using only one value to estimate the population easily leads to the incorrect estimation. Therefore, the research for classification of mixed data using Naive Bayes classifiers is not very successful. In this paper, we propose a classification method, Extended Naive Bayes (ENB), which is capable for handling mixed data. The experimental results have demonstrated the efficiency of our algorithm in comparison with other classification algorithms ex. CART, DT and MLP's.