Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers

Borchani, Hanen; Larrañaga Múgica, Pedro María; Gama, João y Bielza Lozoya, María Concepción (2016). Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. "Intelligent data analysis", v. 20 (n. 2); pp. 257-280. ISSN 1088-467X. https://doi.org/10.3233/IDA-160804.

Descripción

Título: Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers
Autor/es:
  • Borchani, Hanen
  • Larrañaga Múgica, Pedro María
  • Gama, João
  • Bielza Lozoya, María Concepción
Tipo de Documento: Artículo
Título de Revista/Publicación: Intelligent data analysis
Fecha: 2016
Volumen: 20
Materias:
Palabras Clave Informales: Multi-dimensional Bayesian network classifiers; Stream data mining; Adaptive learning;, Concept drift
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In recent years, a plethora of approaches have been proposed to deal with the increasingly challenging task of mining concept-drifting data streams. However, most of these approaches can only be applied to uni-dimensional classification problems where each input instance has to be assigned to a single output class variable. The problem of mining multi-dimensional data streams, which includes multiple output class variables, is largely unexplored and only few streaming multi-dimensional approaches have been recently introduced. In this paper, we propose a novel adaptive method, named Locally Adaptive-MB-MBC (LA-MB-MBC), for mining streaming multi-dimensional data. To this end, we make use of multi-dimensional Bayesian network classifiers (MBCs) as models. Basically, LA-MB-MBC monitors the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a concept drift is detected, LA-MB-MBC adapts the current MBC network locally around each changed node. An experimental study carried out using synthetic multi-dimensional data streams shows the merits of the proposed method in terms of concept drift detection as well as classification performance.

Más información

ID de Registro: 46290
Identificador DC: http://oa.upm.es/46290/
Identificador OAI: oai:oa.upm.es:46290
Identificador DOI: 10.3233/IDA-160804
URL Oficial: http://content.iospress.com/articles/intelligent-data-analysis/ida804
Depositado por: Memoria Investigacion
Depositado el: 17 Oct 2017 09:01
Ultima Modificación: 17 Oct 2017 09:01
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