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Authors: Rabaa Alabdulrahman 1 ; Herna Viktor 1 and Eric Paquet 2

Affiliations: 1 University of Ottawa, Canada ; 2 National Research Council of Canada and University of Ottawa, Canada

ISBN: 978-989-758-203-5

Keyword(s): Online Learning, Data Streams, Active Ensemble Learning. Oracle.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Interactive and Online Data Mining ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Data streams, where an instance is only seen once and where a limited amount of data can be buffered for processing at a later time, are omnipresent in today’s real-world applications. In this context, adaptive online ensembles that are able to learn incrementally have been developed. However, the issue of handling data that arrives asynchronously has not received enough attention. Often, the true class label arrives after with a time-lag, which is problematic for existing adaptive learning techniques. It is not realistic to require that all class labels be made available at training time. This issue is further complicated by the presence of late-arriving, slowly changing dimensions (i.e., late-arriving descriptive attributes). The aim of active learning is to construct accurate models when few labels are available. Thus, active learning has been proposed as a way to obtain such missing labels in a data stream classification setting. To this end, this paper introduces an active online ensemble (AOE) algorithm that extends online ensembles with an active learning component. Our experimental results demonstrate that our AOE algorithm builds accurate models against much smaller ensemble sizes, when compared to traditional ensemble learning algorithms. Further, our models are constructed against small, incremental data sets, thus reducing the number of examples that are required to build accurate ensembles. (More)

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Paper citation in several formats:
Alabdulrahman, R.; Viktor, H. and Paquet, E. (2016). An Active Learning Approach for Ensemble-based Data Stream Mining.In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 275-282. DOI: 10.5220/0006047402750282

@conference{kdir16,
author={Rabaa Alabdulrahman. and Herna Viktor. and Eric Paquet.},
title={An Active Learning Approach for Ensemble-based Data Stream Mining},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={275-282},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006047402750282},
isbn={978-989-758-203-5},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - An Active Learning Approach for Ensemble-based Data Stream Mining
SN - 978-989-758-203-5
AU - Alabdulrahman, R.
AU - Viktor, H.
AU - Paquet, E.
PY - 2016
SP - 275
EP - 282
DO - 10.5220/0006047402750282

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