Name
Abstract | ||
|---|---|---|
The naive Bayes is a competitive classifier that makes strong conditional independence assumptions. Its accuracy can be improved by relaxing these assumptions. One classifier which does that is the semi-naive Bayes. The state-of-the-art algorithm for learning a semi-naive Bayes from data is the backward sequential elimination and joining (BSEJ) algorithm. We extend BSEJ with a second step which removes some of its unwarranted independence assumptions. Our classifier outperforms BSEJ and five other Bayesian network classifiers on a set of benchmark databases, although the difference in performance is not statistically significant. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40643-0_17 | ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2013 |
Keywords | Field | DocType |
semi-naive Bayes,tree augmented naive Bayes,Bayesian network classifiers | Pattern recognition,Naive Bayes classifier,Conditional independence,Computer science,Bayesian network,Artificial intelligence,Classifier (linguistics),Bayes error rate,Bayes classifier,Bayes' theorem,Quadratic classifier | Conference |
Volume | ISSN | Citations |
8109 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bojan Mihaljevic | 1 | 8 | 2.91 |
| Pedro Larrañaga | 2 | 3882 | 208.54 |
| Concha Bielza | 3 | 909 | 72.11 |