Multinomial naive Bayes for text categorization revisited

About this item

Title

Multinomial naive Bayes for text categorization revisited

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

This paper presents empirical results for several versions of the multinomial naive Bayes classifier on four text categorization problems, and a way of improving it using locally weighted learning. More specifically, it compares standard multinomial naive Bayes to the recently proposed transformed weight-normalized complement naive Bayes classifier (TWCNB) [1], and shows that some of the modifications included in TWCNB may not be necessary to achieve optimum performance on some datasets. Howe...

Read more

Format

Research Paper

Research format

Conference item

Date created

2005

Creator

Kibriya, Ashraf Masood / Frank, Eibe / Pfahringer, Bernhard / Holmes, Geoffrey

URL

https://hdl.handle.net/10289/1448

Related subjects

computer science / multinomial naive Bayes classifier / Machine learning

What is the copyright status of this item?

All Rights Reserved

This item is all rights reserved, which means you'll have to get permission from University of Waikato before using it.

More Information

University of Waikato has this to say about the rights status of this item:

©2023 The Author(s). This is an open-access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).

You can learn more about the rights status of this item at: https://researchcommons.waikato.ac.nz/pages/copyright_reuse/en

What can I do with this item?

You must always check with University of Waikato to confirm the specific terms of use, but this is our understanding:

Non-infringing use

NZ Copyright law does not prevent every use of a copyright work. You should consider what you can and cannot do with a copyright work.

No sharing

You may not copy and/or share this item with others without further permission. This includes posting it on your blog, using it in a presentation, or any other public use.

No modifying

You are not allowed to adapt or remix this item into any other works.

No commercial use

You may not use this item commercially.