posted on 2004-01-01, 00:00authored byHonghua Dai, Gang LiGang Li, Z H Zhou
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.
History
Journal
Lecture notes in computer science
Volume
3056
Pagination
260 - 271
Publisher
Springer-Verlag
Location
Berlin, Germany
ISSN
0302-9743
eISSN
1611-3349
Language
eng
Notes
Book Title: Advances in knowledge discovery and data mining