Time–Adaptive Support Vector Machines

In this work we propose an adaptive classification method able both to learn and to follow the temporal evolution of a drifting concept. With that purpose we introduce a modified SVM classifier, created using multiple hyperplanes valid only at small temporal intervals (windows). In contrast to oth...

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Detalles Bibliográficos
Autores principales: Grinblat, Guillermo, Granitto, Pablo M., Ceccatto, Alejandro
Formato: Artículo
Lenguaje:en_US
Publicado: Asociación Española de Inteligencia Artificial 2011
Materias:
Acceso en línea:http://hdl.handle.net/2133/1718
http://hdl.handle.net/2133/1718
Aporte de:Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR) de Universidad Nacional de Rosario Ver origen
Descripción
Sumario:In this work we propose an adaptive classification method able both to learn and to follow the temporal evolution of a drifting concept. With that purpose we introduce a modified SVM classifier, created using multiple hyperplanes valid only at small temporal intervals (windows). In contrast to other strategies proposed in the literature, our method learns all hyperplanes in a global way, minimizing a cost function that evaluates the error committed by this family of local classifiers plus a measure associated to the VC dimension of the family. We also show how the idea of slowly changing classifiers can be applied to non-linear stationary concepts with results similar to those obtained with normal SVMs using gaussian kernels.