JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2011, Vol. 41 ›› Issue (4): 44-48.

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A method of fuzzy integral ensemble classifiers for handling concept-drifting data streams

JU Chun-hua1,2, CHEN Zhi-qi1*   

  1. 1. School of Computer Science ＆ Information Engineering;
    2.Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China
  • Received:2011-02-14 Online:2011-08-16 Published:2011-02-14

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Abstract:

A new classification algorithm FI-MDS based on fuzzy integral fusion was proposed, which aimed at mining data steams with concept drifts and noise and  combined fuzzy integral fusion and ensemble multi-classifiers technology. First, the decision-making profile could  be obtained by training samples through base classifiers, and then  the final classification result could be obtained via fuzzy integral fusion. Meanwhile, a dynamic weight update was  also introduced to improve the adaptability of this algorithm. The experiment results indicated that this method could  enhance the detection accuracy of the concept drifts. Complex classification problems in data streams could  be solved and the algorithm has higher classification performance, effectiveness and robustness.

Key words: data mining, data streams, concept drift, fuzzy integral

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