|
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. | |
|
International
|
No |
|
Congress
|
XV Conferencia de la Asociación Española para la Inteligencia Artificial |
|
|
960 |
|
Place
|
Madrid |
|
Reviewers
|
Si |
|
ISBN/ISSN
|
978-3-642-40642-3 |
|
|
|
|
Start Date
|
17/09/2013 |
|
End Date
|
20/09/2013 |
|
From page
|
159 |
|
To page
|
167 |
|
|
Advances in Artificial Intelligence, Proceedings of the 15th MultiConference of the Spanish Association for Artificial Intelligence, volume 8109 of Lecture Notes in Computer Science |