Identifying product failure rate based on a conditional Bayesian network classifier

Identifying product failure rate based on a conditional Bayesian network classifier

Fast Conditional Independence-based Bayesian Classifier

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier BC), it is po...

ABN: A Fast, Greedy Bayesian Network 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.

A New Restricted Bayesian Network Classifier

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.

Arrhythmia Identification from ECG Signals with a Neural Network Classifier Based on a Bayesian Framework

This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimenta...