Recent work in supervised learning has shown that a surprisingly simple Bayesian classiier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classiiers such as C4.5. This fact raises the question of whether a classiier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classi...

Multi-label classification is one of the important research areas in data mining. In this paper, a new multilabel classification method using multinomial naive Bayes is proposed. We use a new fine-grained weighting method for calculating the weights of feature values in multinomial naive Bayes. Our experiments show that the value weighting method could improve the performance of multinomial nai...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

The Naive Bayes classifier is a simple and accurate classifier. This paper shows that assuming the Naive Bayes classifier model and applying Bayesian model averaging and the principle of indifference, an equally simple, more accurate and theoretically well founded classifier can be obtained. Introduction In this paper we use Bayesian model averaging and the principle of indifference to derive a...

The Bayesian network formalism is becoming increasingly popular in many areas such as decision aid or diagnosis, in particular thanks to its inference capabilities, even when data are incomplete. For classification tasks, Naive Bayes and Augmented Naive Bayes classifiers have shown excellent performances. Learning a Naive Bayes classifier from incomplete datasets is not difficult as only parame...

The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data characteristics which affect the performance of naive Bayes. Our approach uses Monte Carlo simulations that allow...

Naive Bayes (NB) classifier is a simple and efficient classifier, but the independent assumption of its attribute limits the application of the actual data. This paper presents an approach called particle swarm optimization-naive Bayes (PSO-NB) which takes advantage of combination particle swarm optimization with naive Bayes for attribute selection to improve naive Bayes classifier. This method...

Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffding Trees—Hoeffding Trees with naive Bayes models at the leaf nodes—albeit with increased runtime compared to standard Hoeffding Trees. In this paper, we show that runtime can be reduced by replacing naive Bayes with perceptron c...