Brunel University Research Archive (BURA) >
Research Areas >
Computer Science >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3226

Title: Evolutionary learning of dynamic probabilistic models with large time lags
Authors: Tucker, A
Liu, X
Ogden-Swif, A
Publication Date: 2001
Publisher: Wiley
Citation: International Journal of Intelligent Systems. 16 (5) 621-645
Abstract: In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations.
URI: http://bura.brunel.ac.uk/handle/2438/3226
Appears in Collections:School of Information Systems, Computing and Mathematics Research Papers
Computer Science

Files in This Item:

File Description SizeFormat
Article_info.txt233 BTextView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan