A multi-swarm synergetic optimizer for multi-knowledge extraction using rough set

Title:

A multi-swarm synergetic optimizer for multi-knowledge extraction using rough set

Creator:

Yue, Benxian, Liu , Hongbo, Abraham , Ajith, and Badr, Youakim

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:657ca074-86cc-4c11-a9ff-d32d74c7c201
uuid:657ca074-86cc-4c11-a9ff-d32d74c7c201

Subject:

Swarm intelligence, rough set, particle swarm optimization, and multi-knowledge extraction

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Finding reducts is one of the key problems in the increasing applications of rough set theory, which is also one of the bottlenecks of the rough set methodology. The population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this paper, we design a multi-swarm synergetic optimization algorithm (MSSO) for rough set reduction and multi-knowledge extraction. It is a multi-swarm based search approach, in which different individual trends to be encoded to different reduct. The approach discovers the best feature combinations in an efficient way to observe the change of positive region as the particles proceed throughout the search space. The performance of our approach is evaluated and compared with Standard Particle Swarm Optimization (SPSO) and Genetic Algorithms (GA). Empirical results illustrate that the approach can be applied for multiple reduct problems and multi-knowledge extraction effectively.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:4

Harvested from:

CDK

Metadata only:

false