dc.creator | Oikonomou, P. | en |
dc.creator | Papageorgiou, E. I. | en |
dc.date.accessioned | 2015-11-23T10:41:45Z | |
dc.date.available | 2015-11-23T10:41:45Z | |
dc.date.issued | 2013 | |
dc.identifier | 10.1007/978-3-642-41142-7_52 | |
dc.identifier.isbn | 9783642411410 | |
dc.identifier.issn | 18684238 | |
dc.identifier.uri | http://hdl.handle.net/11615/31484 | |
dc.description.abstract | The task of classification using intelligent methods and learning algorithms is a difficult task leading the research community on finding new classifications techniques to solve it. In this work, a new approach based on particle swarm optimization (PSO) clustering is proposed to perform the fuzzy cognitive map learning for classification performance. Fuzzy cognitive map (FCM) is a simple, but also powerful computational intelligent technique which is used for the adoption of the human knowledge and/or historical data, into a simple mathematical model for system modeling and analysis. The aim of this study is to investigate a new classification algorithm for the autism disorder problem by integrating the Particle Swarm Optimization method (PSO) in FCM learning, thus producing a higher performance classification tool regarding the accuracy of the classification, and overcoming the limitations of FCMs in the pattern analysis area. © IFIP International Federation for Information Processing 2013. | en |
dc.source.uri | http://www.scopus.com/inward/record.url?eid=2-s2.0-84894103592&partnerID=40&md5=843ff33d34f789bf939df81e402f6025 | |
dc.title | Particle Swarm Optimization approach for fuzzy cognitive maps applied to autism classification | en |
dc.type | other | en |