This article investigates boosting naive Bayesian classification. It first shows that boosting does not improve the accuracy of the naive Bayesian classifier as much as we expected in a set of natural domains. By analyzing the reason for boosting’s weakness, we propose to introduce tree structures into naive Bayesian classification to improve the performance of boosting when working with naive ...

A novel semi-naive Bayesian classifier is introduced that is particularly suitable to data with many attributes. The naive Bayesian classifier is taken as a starting point and correlations are reduced through joining of highly correlated attributes. Our technique differs from related work in its use of kernel-functions that systematically include continuous attributes rather than relying on dis...

On the basis of examining the existing restricted Bayesian network classifiers, a new Bayes-theorem-based and more strictly restricted Bayesian-network-based classification model DLBAN is proposed, which can be viewed as a double-level Bayesian network augmented naive Bayes classification. The experimental results show that the DLBAN classifier is better than the TAN classifier in the most cases.

In this work the analysis of branches of Ukrainian economy was done, particularly average financial parameters were found. For each parameter the boundaries were determined which divide enterprises into 5 parts and allow making more detailed ratings. The ratings were made by each parameter and then the aggregate rating was found. The analysis of indices interrelation was made using Bayesian net...

The simple Bayesian classi er (SBC) is commonly thought to assume that attributes are independent given the class, but this is apparently contradicted by the surprisingly good performance it exhibits in many domains that contain clear attribute dependences. No explanation for this has been proposed so far. In this paper we show that the SBC does not in fact assume attribute independence, and ca...

Negotiation is one of the most fundamental and effective mechanism for resolving conflicts between self-interested agents and producing mutually acceptable compromises. Most existing research in negotiation presumes a fixed negotiation context which cannot be changed during the process of negotiation and that the agents have complete and correct knowledge about all aspects of the issues being n...

The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning. 2004 Elsevier B.V. All rights reserved.

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, ...