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Memorias de investigación
Ponencias en congresos:
Mining concept-drifting data streams containing labeled and unlabeled instances
Año:2010
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Recently, mining data streams has attracted significant attention and has been considered as a challenging task in supervised classification. Most of the existing methods dealing with this problem assume the availability of entirely labeled data streams. Unfortunately, such assumption is often violated in real-world applications given that obtaining labels is a time-consuming and expensive task, while a large amount of unlabeled instances are readily available. In this paper, we propose a new approach for handling concept-drifting data streams containing labeled and unlabeled instances. First, we use KL divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classifier is learned using the EM algorithm; otherwise, the current classifier is kept unchanged. Our approach is general so that it can be applied with different classification models. Experiments performed with naive Bayes and logistic regression, on two benchmark datasets, show the good performance of our approach using only limited amounts of labeled instances.
Internacional
Si
Nombre congreso
23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010
Tipo de participación
960
Lugar del congreso
Córdoba, España
Revisores
Si
ISBN o ISSN
3-642-13021-6
DOI
10.1007/978-3-642-13022-9_53
Fecha inicio congreso
01/06/2010
Fecha fin congreso
04/06/2010
Desde la página
531
Hasta la página
540
Título de las actas
Trends in Applied Intelligent Systems
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Hanen Borchani (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
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