Tracking recurrent concepts using context

Bártolo Gomes, Joao Paulo and Menasalvas Ruiz, Ernestina and Sousa, Pedro (2012). Tracking recurrent concepts using context. "Intelligent data analysis", v. 16 (n. 5); pp. 803-825. ISSN 1088-467X.


Title: Tracking recurrent concepts using context
  • Bártolo Gomes, Joao Paulo
  • Menasalvas Ruiz, Ernestina
  • Sousa, Pedro
Item Type: Article
Título de Revista/Publicación: Intelligent data analysis
Date: 2012
ISSN: 1088-467X
Volume: 16
Freetext Keywords: 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.
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

More information

Item ID: 15535
DC Identifier:
OAI Identifier:
DOI: 10.3233/IDA-2012-0552
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 03 Jun 2013 17:11
Last Modified: 21 Apr 2016 15:38
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