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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2016

Autores

Borchani, H (Autor) Outra; Larranaga, P (Autor) Outra; João Gama (Autor) FEP; Bielza, C (Autor) Outra

Revista

Título: Intelligent Data Analysis

Vol. 20 Nº 2

Páginas: 257-280

ISSN: 1088-467X

Editora: IOS PRESS

Indexação

ISI Web of Knowledge - 13 Citações

Scopus - 14 Citações

Outras Informações

ID Authenticus: P-00K-9AN

DOI: 10.3233/ida-160804

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 24