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Authors: Runxin Wang ; Lei Shi ; Mícheál Ó. Foghlú and Eric Robson

Affiliation: Telecommunications Software & Systems Group Waterford Institute of Technology, Ireland

ISBN: 978-989-8425-28-7

Keyword(s): Data Mining, Supervised Learning, Concept Drift, Meta-Learning, Evolving Data.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our method, a MSL learner is structured in a recursive manner, which contains all the base learners maintained in a hierarchy, ensuring the learned concepts are traceable. We evaluated MSL and two ensemble techniques on three synthetic datasets, which contain a number of drastic concept drifts. The experimental results show that the proposed method generally performs better than the ensemble techniques in terms of prediction accuracy.

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Paper citation in several formats:
Wang, R.; Shi, L.; Foghlú, M. and Robson, E. (2010). A META-LEARNING METHOD FOR CONCEPT DRIFT.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 257-262. DOI: 10.5220/0003095502570262

@conference{kdir10,
author={Runxin Wang. and Lei Shi. and Mícheál Ó. Foghlú. and Eric Robson.},
title={A META-LEARNING METHOD FOR CONCEPT DRIFT},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={257-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003095502570262},
isbn={978-989-8425-28-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - A META-LEARNING METHOD FOR CONCEPT DRIFT
SN - 978-989-8425-28-7
AU - Wang, R.
AU - Shi, L.
AU - Foghlú, M.
AU - Robson, E.
PY - 2010
SP - 257
EP - 262
DO - 10.5220/0003095502570262

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