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 naïve 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.
Original language | English (US) |
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Pages (from-to) | 157-179 |
Number of pages | 23 |
Journal | Knowledge and Information Systems |
Volume | 9 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2006 |
All Science Journal Classification (ASJC) codes
- Software
- Information Systems
- Human-Computer Interaction
- Hardware and Architecture
- Artificial Intelligence