Memorias de investigación
Artículos en revistas:
Tracking recurrent concepts using context
Año:2012
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.
Internacional
Si
JCR del ISI
Si
Título de la revista
Intelligent Data Analysis
ISSN
1088-467X
Factor de impacto JCR
0,448
Información de impacto
Datos JCR del año 2011
Volumen
16
DOI
10.3233/IDA-2012-0552
Número de revista
5
Desde la página
803
Hasta la página
825
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Data Mining Engineering (DaME) Ingeniería de Minería de datos