AVT-NBL: An Algorithm for Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data
Jun Zhang and Vasant Honavar
Artificial Intelligence Research Laboratory
Iowa State University, USA
|
Abstract |
|---|
|
In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT) - hierarchical groupings of attribute values - to learn compact, comprehensible, and accurate classifiers from data - including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the Naive Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples. |
|
Presentation Slides: [ppt] |
|
AVT-based Naive Bayes Learning Algorithm in Java |
|---|
|
Source Code Download: [AVT-NBL 1.4] |
|
AVTs and Data |
|---|
|
AVTs Download: [avts] Data Sets Download: [data] |
|
|
Copyright(c) 2004 Jun Zhang. All rights reserved.