Loading...
Please wait, while we are loading the content...
Similar Documents
Comparing Bayesian Network Classifiers
| Content Provider | CiteSeerX |
|---|---|
| Abstract | In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers – Naïve-Bayes, tree augmented Naïve-Bayes, BN augmented Naïve-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BNlearning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Computational Time Bayesian Network Bn Classifier Machine Learning New Algorithm Na Ve-bayes Obtained Classifier Classifier Na Ve-bayes Known Classifier Bnlearning Algorithm Effective Classifier Bayesian Network Classifier Experimental Result General Bns Data Mining Community |
| Content Type | Text |
| Resource Type | Article |
