We report on an empirical study of supervised learning algorithms that induce models to resolve the meaning of ambiguous words in text. We find that the Naive Bayesian classifier is as accurate as several more sophisticated methods. This is a surprising result since Naive Bayes makes simplifying assumptions about disambiguation that are not realistic. However, our results correspond to a growin...

In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that ...

We present a new Bayesian classifier for computer-aided diagnosis. The new classifier builds upon the naive-Bayes classifier, and models the dependencies among patient findings in an attempt to improve its performance, both in terms of classification accuracy and in terms of calibration of the estimated probabilities. This work finds motivation in the argument that highly calibrated probabiliti...

Classification is the problem of predicting the class of a given instance, on the basis of some attributes (features) of it. In the Bayesian framework 1, a classifier is learned from data by updating a prior density, which represents the beliefs before analyzing the data and which is usually assumed uniform, with the likelihood, which models the evidence coming from the data; this yields a post...

Using probabilistic learning, we develop a naive Bayesian classifier to passively infer a host’s operating system from packet headers. We analyze traffic captured from an Internet exchange point and compare our classifier to rule-based inference tools. While the host operating system distribution is heavily skewed, we find operating systems that constitute a small fraction of the host count con...

Bayesian content filters gained popular acclaim when they were put forward in 2002 by Paul Graham as a potential long-term solution for the spam problem. They have since fallen from the limelight, however, due to perceived attack vulnerabilities inherent to all content-based filters as well as real and imagined vulnerabilities specific to Bayesian filters. It has also been assumed that Bayesian...

This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier.

Adaptive Bayes Network (ABN) is a fast algorithm for constructing Bayesian Network classifiers using Minimum Description Length (MDL) and automatic feature selection. ABN does well in domains where Naive Bayes fares poorly, and in other domains is, within statistical bounds, at least as good a classifier.