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Learning Pseudo-independent Models: Analytical and Experimental Results
Book chapter   Peer reviewed

Learning Pseudo-independent Models: Analytical and Experimental Results

Yang Xiang, Xiaohua Hu, Nicholas J. Cercone and Howard J. Hamilton
Advances in Artificial Intelligence
19 May 2000
DOI: https://doi.org/10.1007/3-540-45486-1_19

Abstract

belief networks data mining learning uncertainty
Most algorithms to learn belief networks use single-link looka-head search to be efficient. It has been shown that such search procedures are problematic when applied to learning pseudo-independent (PI) models. Furthermore, some researchers have questioned whether PI models exist in practice. We present two non-trivial PI models which derive from a social study dataset. For one of them, the learned PI model reached ultimate prediction accuracy achievable given the data only, while using slightly more inference time than the learned non-PI model. These models provide evidence that PI models are not simply mathematical constructs. To develop efficient algorithms to learn PI models effectively we benefit from studying and understanding such models in depth. We further analyze how multiple PI submodels may interact in a larger domain model. Using this result, we show that the RML algorithm for learning PI models can learn more complex PI models than previously known.

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5 citations in Web of Science
11 citations in Scopus

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Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
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