Journal
INTELLIGENT DATA ANALYSIS
Volume 20, Issue 2, Pages 257-280Publisher
IOS PRESS
DOI: 10.3233/IDA-160804
Keywords
Multi-dimensional Bayesian network classifiers; stream data mining; adaptive learning; concept drift
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Funding
- Spanish Ministry of Economy and Competitiveness through the Cajal Blue Brain [C080020-09]
- Spanish Ministry of Economy and Competitiveness (Spanish partner of the Blue Brain initiative from EPFL)
- Regional Government of Madrid [S2013/ICE-2845-CASI-CAM-CM]
- European Union [604102]
- [TIN2013-41592-P]
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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.
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