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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.
Title: | Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers |
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Author/s: |
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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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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.
Type | Code | Acronym | Leader | Title |
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Government of Spain | C080020-09 | Unspecified | Unspecified | Cajal Blue Brain |
Government of Spain | TIN2013-41592-P | Unspecified | Unspecified | Aprendizaje de redes bayesianas con variables sin y con direccionalidad para descubrimiento de asociaciones, predicción multirespuesta y clustering |
Madrid Regional Government | S2013/ICE-2845 | CASI – CAM | Pedro Larrañaga Mujica | Conceptos y aplicaciones de los sistemas inteligentes |
FP7 | 604102 | HBP | Unspecified | The Human Brain Project |
Item ID: | 46290 |
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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 |