Journal
GENETICS AND MOLECULAR RESEARCH
Volume 12, Issue 4, Pages 4468-4479Publisher
FUNPEC-EDITORA
DOI: 10.4238/2013.October.10.12
Keywords
Bayesian network structure learning; Convergence; Particle swarm optimization; Chaos theory; Ergodicity
Funding
- National Natural Science Foundation of China [31370778, 31170797, 30870573, 61103057]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1109]
- Program for Liaoning Excellent Talents in University [LR201003]
- Program for Liaoning Science and Technology Research in University [LS2010179]
Ask authors/readers for more resources
The Bayesian network (BN) is a knowledge representation form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal relationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most successful. Nonetheless, the performance of the K2 algorithm is greatly affected by a prior ordering of input nodes. The proposed method in this paper is based on the chaotic particle swarm optimization (CPSO) and the K2 algorithm. Because the PSO algorithm completely entraps the local minimum in later evolutions, we combined the PSO algorithm with the chaos theory, which has the properties of ergodicity, randomness, and regularity. Experimental results show that the proposed method can improve the convergence rate of particles and identify networks more efficiently and accurately.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available