Local decision making and decision fusion in hierarchical levels
No Thumbnail Available
Date
2009
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
Hierarchical problem solving is preferred when the problem is overwhelmingly complicated. In such a case, the problem should better be analyzed in hierarchical levels. At each level, some temporary solutions are obtained; then a suitable decision fusion technique is used to merge the temporary solutions for the next level. The hierarchical framework proposed in this study depends on reutilization or elimination of previous level local agents that together perform the decisions due to a decision-fusion technique: a performance criterion is set for local agents. The criterion checks the success of agents in their local regions. An agent satisfying this criterion is reutilized in the next level, whereas an agent not successful enough is removed from the agent pool in the next level. In place of a removed agent, a number of new local agents are developed. This framework is applied on a fault detection problem.
Description
Leblebicioglu, Mehmet Kemal/0000-0002-9735-458X
Keywords
Decision-Making, Decision-Fusion, Neural Networks, Classification, Machine Learning
Turkish CoHE Thesis Center URL
Fields of Science
Citation
Beldek,Ulaş; Leblebicioglu, Kemal, "Local decision making and decision fusion in hierarchical levels",Top, Vol.17, No.1, pp.44-69, (2009).
WoS Q
Q4
Scopus Q
Q2
Source
International Workshop on Operational Research Conference held in honor of Laureano F Escudero -- JUN 05-07, 2008 -- Madrid, SPAIN
Volume
17
Issue
1
Start Page
44
End Page
69