Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-toNoise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify ...

In this paper a hybrid adaptive particle swarm optimization aided learnable Bayesian classifier is proposed. The objective of the classifier is to solve some of the fundamental problems associated with the pure naive Bayesian classifier and its variants with a larger view towards maximization of the classifier accuracy. Further, the proposed algorithm can exhibits an improved capability to elim...

We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means there exists a learning problem such that for all amounts of data the generalization errors of the MDL classifier and the Bayes classifier relative to the Bayesian posterior both remain bounded away from the smallest achievable generalization error.

Bayesian network (BN) classifiers use different structures and different training parameters which leads to diversity in classification decisions. This work empirically shows that building an ensemble of several fine-tuned BN classifiers increases the overall classification accuracy. The accuracy of the constituent classifiers can be achieved by fine-tuning each classifier and the diversity is ...

A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set into positive, negative and boundary regions based on an equivalence relation on the universe. In this paper, ...