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A random method for quantifying changing distributions in data streams

Publication ,  Conference
Wang, H; Pei, J
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2005

In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between two datasets with class labels. Traditionally, changes are often measured by first estimating the probability distributions of the given data, and then computing the distance, for instance, the K-L divergence, between the estimated distributions. However, this approach is computationally infeasible for large, high dimensional datasets. The problem becomes more challenging in the streaming data environment, as the high speed makes it difficult for the learning process to keep up with the concept drifts in the data. To tackle this problem, we propose a method to quantify concept drifts using a universal model that incurs minimal learning cost. In addition, our model also provides the ability of performing classification. © Springer-Verlag Berlin Heidelberg 2005.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540292449

Publication Date

January 1, 2005

Volume

3721 LNAI

Start / End Page

684 / 691

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Wang, H., & Pei, J. (2005). A random method for quantifying changing distributions in data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3721 LNAI, pp. 684–691). https://doi.org/10.1007/11564126_73
Wang, H., and J. Pei. “A random method for quantifying changing distributions in data streams.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3721 LNAI:684–91, 2005. https://doi.org/10.1007/11564126_73.
Wang H, Pei J. A random method for quantifying changing distributions in data streams. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 684–91.
Wang, H., and J. Pei. “A random method for quantifying changing distributions in data streams.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3721 LNAI, 2005, pp. 684–91. Scopus, doi:10.1007/11564126_73.
Wang H, Pei J. A random method for quantifying changing distributions in data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2005. p. 684–691.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540292449

Publication Date

January 1, 2005

Volume

3721 LNAI

Start / End Page

684 / 691

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences