Tracking recurrent concepts using context

Bártolo Gomes, Joao Paulo; Menasalvas Ruiz, Ernestina y Sousa, Pedro (2012). Tracking recurrent concepts using context. "Intelligent data analysis", v. 16 (n. 5); pp. 803-825. ISSN 1088-467X. https://doi.org/10.3233/IDA-2012-0552.

Descripción

Título: Tracking recurrent concepts using context
Autor/es:
  • Bártolo Gomes, Joao Paulo
  • Menasalvas Ruiz, Ernestina
  • Sousa, Pedro
Tipo de Documento: Artículo
Título de Revista/Publicación: Intelligent data analysis
Fecha: 2012
Volumen: 16
Materias:
Palabras Clave Informales: Data stream mining, concept drift, recurring concepts, context-awareness, ubiquitous knowledge discovery, extracción del flujo de datos, conceptos recurrentes, conciencia del contexto, descubrimiento del conocimiento global.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 15535
Identificador DC: http://oa.upm.es/15535/
Identificador OAI: oai:oa.upm.es:15535
Identificador DOI: 10.3233/IDA-2012-0552
URL Oficial: http://iospress.metapress.com/content/am42572h40713424/
Depositado por: Memoria Investigacion
Depositado el: 03 Jun 2013 17:11
Ultima Modificación: 21 Abr 2016 15:38
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