We investigate why discretization is effective in naive-Bayes learning. We prove a theorem that identifies particular conditions under which discretization will result in naiveBayes classifiers delivering the same probability estimates as would be obtained if the correct probability density functions were employed. We discuss the factors that might affect naive-Bayes classification error under ...

In this paper, we have presented a survey on the different data mining technique of intrusion detection which is basically used for the intrusion detection purpose in the field of data mining. Today intrusion detection in data mining has gain more interest to the researches, there are many intrusion detection issues in data mining like dos attacks, R2L, U2R and probing etc. There are many algor...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...

This paper gives an algebraic derivation of the posterior for both the noisy-or and naive Bayes models, as a function of both input messages and probability table parameters. By examining these functions we show a technique where the naive Bayes model may be used to approximate a logical-OR, rather than its typical interpretation as a logicalAND. The technique is to avoid the use of disconfirmi...

In standard classification a training set of supervised instances is given. In a more general setup, some supervised instances are available, while further ones should be chosen from an unsupervised set and then annotated. As the annotation step is costly, active learning algorithms are used to select which instances to annotate to maximally increase the classification performance while annotat...

Using naive Bayes for email classification has become very popular within the last few months. They are quite easy to implement and very efficient. In this paper we want to present empirical results of email classification using a combination of naive Bayes and k-nearest neighbor searches. Using this technique we show that the accuracy of a Bayes filter can be improved slightly for a high numbe...

We review some approaches to qualitative uncertainty and propose a new one based on the idea of Absolute Order of Magnitude. We show that our ideas can be useful for Knowledge Discovery by introducing a derivation of the Naive-Bayes classifier based on them: the Qualitative Bayes Classifier. This classification method keeps Naive-Bayes accuracy while gaining interpretability, so we think it can...

Naive Bayes classifiers estimate posterior probabilities poorly (Zhang, 2004). In this paper, we propose a modification to the Naive Bayes classification algorithm which improves the classifier’s posterior probability estimates without affecting its performance. Since the modification involves the use of the reciprocal of the perplexity of the class-conditional feature probabilities, we call th...

Multinomial naive Bayes (MNB) assumes that all attributes (i.e., features) are independent of each other given the context of the class, and it ignores all dependencies among attributes. However, in many real-world applications, the attribute independence assumption required by MNB is often violated and thus harms its performance. To weaken this assumption, one of the most direct ways is to ext...