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

Borchani, Hanen and Larrañaga Múgica, Pedro María and Gama, João and 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.

Description

Title: Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers
Author/s:
  • Borchani, Hanen
  • Larrañaga Múgica, Pedro María
  • Gama, João
  • Bielza Lozoya, María Concepción
Item Type: Article
Título de Revista/Publicación: Intelligent data analysis
Date: 2016
ISSN: 1088-467X
Volume: 20
Subjects:
Freetext Keywords: Multi-dimensional Bayesian network classifiers; Stream data mining; Adaptive learning;, Concept drift
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2013-41592-PUnspecifiedUnspecifiedAprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMPedro Larrañaga MujicaConceptos y aplicaciones de los sistemas inteligentes
FP7604102HBPUnspecifiedThe Human Brain Project

More information

Item ID: 46290
DC Identifier: http://oa.upm.es/46290/
OAI Identifier: oai:oa.upm.es:46290
DOI: 10.3233/IDA-160804
Official URL: http://content.iospress.com/articles/intelligent-data-analysis/ida804
Deposited by: Memoria Investigacion
Deposited on: 17 Oct 2017 09:01
Last Modified: 22 Mar 2019 15:15
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