In this paper, we introduce a new restricted Bayesian network classifier that extends naive Bayes by relaxing the conditional independence assumptions, and show that it is partly generative and partly discriminative. Experimental results show that the hybrid classifier performs better than a purely generative classifier (naive Bayes) or a purely discriminative classifier (Logistic Regression) a...

To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration...

This paper presents the development of a Bayes net classifier for prediction of a victimization attribute value for the National Crime Victimization Survey dataset. The National Crime Victimization Survey dataset has over 250 attributes and 216,000 data points, and as such poses a large-scale problem context for classifier development. The classifier was developed using the Weka machine learnin...

The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag Of Words representation of an email is widel...

The paper proposes a hybrid feature selection approach based on Rough sets and Bayesian network classifiers. In the approach, the classification result of a Bayesian network is used as the criterion for the optimal feature subset selection. The Bayesian network classifier used in the paper is a kind of naive Bayesian classifier. It is employed to implement classification by learning the samples...

In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the de...

We consider the problem of detecting rooftops in overhead images, which is one processing step in a building detection system. Currently, the system uses a hand-configured linear classifier to select the most promising rooftop candidates for further processing. We present results from an empirical study in which we used machine learning methods to acquire the selection criteria for rooftops. RO...