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Learning Bayesian Belief Network Classifiers: Algorithms and System (2001)
| Content Provider | CiteSeerX |
|---|---|
| Author | Cheng, Jie Greiner, Russell |
| Description | This paper investigates the methods for learning predictive classifiers based on Bayesian belief networks (BN) -- primarily unrestricted Bayesian networks and Bayesian multinets. We present our algorithms for learning these classifiers, and discuss how these methods address the overfitting problem and provide a natural method for feature subset selection. Using a set of standard classification problems, we empirically evaluate the performance of various BN-based classifiers. The results show that the proposed BN and Bayes multi-net classifiers are competitive with (or superior to) the best known classifiers, based on both BN and other formalisms; and that the computational time for learning and using these classifiers is relatively small. These results argue that BN based classifiers deserve more attention in the data mining community. 1 In t roduct i on Many tasks -- including fault diagnosis, pattern recognition and forecasting -- can be viewed as classification, as each r... Proceedings of 14 th Biennial conference of the |
| File Format | |
| Language | English |
| Publisher Date | 2001-01-01 |
| Access Restriction | Open |
| Subject Keyword | Computational Time Unrestricted Bayesian Network Fault Diagnosis Bayesian Belief Network Classifier Natural Method Predictive Classifier Various Bn-based Classifier Bayesian Belief Network Standard Classification Problem Pattern Recognition Known Classifier Bayesian Multinets Overfitting Problem Bayes Multi-net Classifier Data Mining Community Many Task |
| Content Type | Text |
| Resource Type | Article |
