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An empirical comparison of Bayesian network parameter learning algorithms for continuous data streams

Ratnapinda, P and Druzdzel, MJ (2013) An empirical comparison of Bayesian network parameter learning algorithms for continuous data streams. In: UNSPECIFIED.

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Abstract

We compare three approaches to learning numerical parameters of Bayesian networks from continuous data streams: (1) the EM algorithm applied to all data, (2) the EM algorithm applied to data increments, and (3) the online EM algorithm. Our results show that learning from all data at each step, whenever feasible, leads to the highest parameter accuracy and model classification accuracy. When facing computational limitations, incremental learning approaches are a reasonable alternative. Of these, online EM is reasonably fast, and similar to the incremental EM algorithm in terms of accuracy. For small data sets, incremental EM seems to lead to better accuracy. When the data size gets large, online EM tends to be more accurate. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Ratnapinda, P
Druzdzel, MJdruzdzel@pitt.eduDRUZDZEL
Date: 13 December 2013
Date Type: Publication
Journal or Publication Title: FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference
Page Range: 627 - 632
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781577356059
Related URLs:
Date Deposited: 25 Jun 2013 16:00
Last Modified: 01 Nov 2017 12:58
URI: http://d-scholarship.pitt.edu/id/eprint/19109

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