Abstract
Naive Bayesian classifiers utilise a simple mathematical model for induction. While it is known that the assumptions on which this model is based are frequently violated, the predictive accuracy obtained in discriminate classification tasks is surprisingly competitive in comparison to more complex induction techniques. Adjusted probability naive Bayesian induction adds a simple extension to the naive Bayesian classifter. A numeric weight is inferred for each class. During discriminate classification, the naive Bayesian probability of a class is multiplied by its weight to obtain an adjusted value. The use of this adjusted value in place of the naive Bayesian probability is shown to significantly improve predictive accuracy.
| Original language | English |
|---|---|
| Title of host publication | Advanced Topics in Artificial Intelligence - 11th Australian Joint Conference on Artificial Intelligence, AI 1998, Selected Papers |
| Editors | Grigoris Antoniou, John Slaney |
| Publisher | Springer |
| Pages | 285-295 |
| Number of pages | 11 |
| ISBN (Print) | 3540651381, 9783540651383 |
| Publication status | Published - 1 Jan 1998 |
| Externally published | Yes |
| Event | Australasian Joint Conference on Artificial Intelligence 1998 - Brisbane, Australia Duration: 13 Jul 1998 → 17 Jul 1998 Conference number: 11th https://link.springer.com/book/10.1007/BFb0095035 (Proceedings) |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 1502 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Australasian Joint Conference on Artificial Intelligence 1998 |
|---|---|
| Abbreviated title | AI 1998 |
| Country/Territory | Australia |
| City | Brisbane |
| Period | 13/07/98 → 17/07/98 |
| Internet address |
|