A PSO-GD-based hybrid algorithm for general fuzzy measure determination

Author

Zhao, Huan-yu ; Wang, Xi-Zhao

Author_Institution

Key Lab. for Machine Learning & Comput. Intell., Hebei Univ., Baoding, China

Volume

1

fYear

2009

Firstpage

553

Lastpage

556

Abstract

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

Keywords

fuzzy set theory; gradient methods; particle swarm optimisation; PSO-GD-based hybrid algorithm; general fuzzy measure determination; gradient descent algorithm; particle swarm optimization; Algorithm design and analysis; Computational intelligence; Computer science; Cybernetics; Educational institutions; Machine learning; Machine learning algorithms; Mathematics; Particle measurements; Particle swarm optimization; Fuzzy integral; Fuzzy measure; Gradient descent algorithm; Particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212478

Filename

5212478