Quick adaptation to changing concepts by sensitive detection

Yoshiaki Yasumura, Naho Kitani, Kuniaki Uehara

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルNew Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings
出版社Springer Verlag
ページ855-864
ページ数10
ISBN(印刷版)9783540733225
DOI
出版ステータスPublished - 2007
外部発表はい
イベント20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007 - Kyoto, Japan
継続期間: 2007 6月 262007 6月 29

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
4570 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007
国/地域Japan
CityKyoto
Period07/6/2607/6/29

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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