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Particle Swarm Optimization Approach for Fuzzy Cognitive Maps Applied to Autism Classification
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.
Cited literature [22 references]
https://hal.inria.fr/hal-01459643
Contributor : Hal Ifip <>
Submitted on : Tuesday, February 7, 2017 - 1:06:18 PM
Last modification on : Thursday, March 5, 2020 - 5:41:29 PM
Long-term archiving on: : Monday, May 8, 2017 - 2:10:03 PM
Contributor : Hal Ifip <>
Submitted on : Tuesday, February 7, 2017 - 1:06:18 PM
Last modification on : Thursday, March 5, 2020 - 5:41:29 PM
Long-term archiving on: : Monday, May 8, 2017 - 2:10:03 PM
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