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 ...

more often clay matrix is the major factor to reduce the porosity and permeability in sandstone facies. consequently determination of clay minerals is of prime importance in reservoir quality assessment. the present study aims to identify four different types of clay mineral namely kaolinite, illite/cholorite, halloysite, and montmorilonite from petrophysical logs (pls) using cation exchange ca...

The Naïve Bayesian Classifier and an Augmented Naïve Bayesian Classifier are applied to human classification tasks. The Naïve Bayesian Classifier is augmented with feature construction using a Galois lattice. The best features, measured on their withinand between-category overlap, are added to the category’s concept description. The results show that space efficient concept descriptions can pre...

OBJECTIVE The objective of our study was to evaluate the feasibility and usefulness of the Bayesian classifier for predicting malignant renal cysts on MDCT. MATERIALS AND METHODS Ninety-three complicated cysts with pathologic confirmation were enrolled. Patient age and sex and seven morphologic features of the cysts including the maximum diameter, wall features, wall thickness, septa features...

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...

In order to defend against extraordinary intelligent attacks in the era of rapidly growing information and technology nowadays, effective and efficient intrusion detection models are needed to detect and prevent intrusion promptly. Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion attacks with reasonable accuracy and effic...

Efficient feature selection from high-dimensional datasets is a very important challenge in many data-driven fields of science and engineering. We introduce statistical mechanics inspired strategy that addresses the problem sparse context binary classification by leveraging computational scheme known as expectation propagation (EP). The algorithm used order to train continuous-weights perceptro...