Ensembling MML causal discovery

Dai, Honghua, Li, Gang and Zhou, Zhi-Hua 2004, Ensembling MML causal discovery, Lecture notes in computer science, vol. 3056, pp. 260-271.

Attached Files
Name Description MIMEType Size Downloads

Title Ensembling MML causal discovery
Author(s) Dai, HonghuaORCID iD for Dai, Honghua orcid.org/0000-0001-9899-7029
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Zhou, Zhi-Hua
Journal name Lecture notes in computer science
Volume number 3056
Start page 260
End page 271
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2004
ISSN 0302-9743
Summary This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.
Notes Book Title: Advances in knowledge discovery and data mining
Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002399

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in TR Web of Science
Scopus Citation Count Cited 9 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 604 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 08:23:39 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.