Quick adaptation to changing concepts by sensitive detection

Authors:
Yoshiaki Yasumura;Naho Kitani;Kuniaki Uehara
Affiliations:
Graduate School of Science and Technology, Kobe University, Kobe, Japan;Graduate School of Science and Technology, Kobe University, Kobe, Japan;Graduate School of Science and Technology, Kobe University, Kobe, Japan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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Abstract

In mining data streams, one of the most challenging tasks is adapting to concept change, that is change over time of the underlying concept in the data. In this paper, we propose a novel ensemble framework for mining concept-changing data streams. This algorithm, called QACC (Quick Adaptation to Changing Concepts), realizes quick adaptation to changing concepts using an ensemble of classifiers. For quick adaptation, QACC sensitively detects concept changes in noisy streaming data. Empirical studies show that the QACC algorithm is efficient for various concept changes.