Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data...

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Detalles Bibliográficos
Autores principales: Khalid, Shehzad, Arshad, Sannia, Jabbar, Sohail, Rho, Seungmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177094/
https://www.ncbi.nlm.nih.gov/pubmed/25295302
http://dx.doi.org/10.1155/2014/492387

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177094/
https://www.ncbi.nlm.nih.gov/pubmed/25295302
http://dx.doi.org/10.1155/2014/492387

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