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

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Borchani, H (Author) Other; Larranaga, P (Author) Other; João Gama (Author) FEP; Bielza, C (Author) Other

Journal

Title: Intelligent Data Analysis

Vol. 20 No. 2

Pages: 257-280

ISSN: 1088-467X

Publisher: IOS PRESS

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 14 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24