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Evolutionary learning of dynamic probabilistic models with large time lags
Author(s) -
Tucker Allan,
Liu Xiaohui,
OgdenSwift Andrew
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1027
Subject(s) - computer science , dynamic bayesian network , artificial intelligence , machine learning , heuristics , bayesian network , simple (philosophy) , probabilistic logic , exploit , representation (politics) , philosophy , computer security , epistemology , politics , political science , law , operating system
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. © 2001 John Wiley & Sons, Inc.