Naïve Bayes for text classification with unbalanced classes

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Title

Naïve Bayes for text classification with unbalanced classes

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute prio...

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Format

Research Paper

Research format

Conference item

Date created

2006

Creator

Frank, Eibe / Bouckaert, Remco R.

URL

https://hdl.handle.net/10289/1442

Related subjects

computer science / Naive Bayes / text classification / Machine learning

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