Name
Abstract | ||
|---|---|---|
Naive Bayes has been an effective and important classifier in the text categorization domain despite violations of its underlying assumptions. Although quite accurate, it tends to provide poor estimates of the posterior class probabilities, which hampers its application in the cost-sensitive context. The apparent high confidence with which certain errors are made is particularly problematic when misclassification costs are highly skewed, since conservative setting of the decision threshold may greatly decrease the classifier utility. We propose an extension of the Naive Bayes algorithm aiming to discount the confidence with which errors are made. The approach is based on measuring the amount of change to feature distribution necessary to reverse the initial classifier decision and can be implemented efficiently without over-complicating the process of Naive Bayes induction. In experiments with three benchmark document collections, the decision-reversal Naive Bayes is demonstrated to substantially improve over the popular multinomial version of the Naive Bayes algorithm, in some cases performing more than 40% better. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11564126_58 | PKDD |
Keywords | Field | DocType |
improved naive bayes,naive bayes algorithm,apparent high confidence,initial classifier decision,decision-reversal naive bayes,classifier utility,benchmark document collection,naive bayes induction,important classifier,misclassification cost,decision threshold,naive bayes | Data mining,Naive Bayes classifier,Computer science,Multinomial distribution,Posterior probability,Knowledge extraction,Classifier (linguistics),Text categorization,Bayes error rate,Bayes classifier | Conference |
Volume | ISSN | ISBN |
3721 | 0302-9743 | 3-540-29244-6 |
Citations | PageRank | References |
2 | 0.37 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aleksander Kołcz | 1 | 628 | 66.65 |
| Abdur Chowdhury | 2 | 2013 | 160.59 |