TY - GEN
T1 - Bayesian network structure learning
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
AU - Zhang, Yikun
AU - LIU, Jiming
AU - LIU, Yang
N1 - Publisher Copyright:
Copyright © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time efficiency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.
AB - In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time efficiency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.
UR - http://www.scopus.com/inward/record.url?scp=85060469081&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85060469081
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 8183
EP - 8184
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
Y2 - 2 February 2018 through 7 February 2018
ER -