Enhancing rare class mining in multimedia big data by concept correlation

Yilin Yan, Mei-Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

The development in information science has enabled an explosive growth of data, which attracts more and more researchers to engage in the field of big data analytics. Noticeably, in many real-world applications, large amounts of data are imbalanced data since the events of interests occur infrequently. However, the detection of these events is such an important research problem and has attracted significant research efforts as lots of real-world big data sets have skewed class distributions. Despite extensive research efforts, rare class mining remains one of the most challenging problems in information science, especially for multimedia big data. Though inter-concept correlations have been utilized to address this issue recently, the very small number of instances in the minority class often lead to the detection of imprecise correlations and unsatisfactory classification results. This paper proposes a novel concept correlation analysis strategy framework using the correlations between the retrieval scores and labels. By integrating the correlation information, the proposed framework can help imbalance data classification and enhance rare class (or concept) mining even with trivial scores from the minority class. Experimental results on the TRECVID multimedia big benchmark data set demonstrate the effectiveness of the proposed framework with promising performance.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages281-286
Number of pages6
ISBN (Electronic)9781509045709
DOIs
StatePublished - Jan 18 2017
Event18th IEEE International Symposium on Multimedia, ISM 2016 - San Jose, United States
Duration: Dec 11 2016Dec 13 2016

Other

Other18th IEEE International Symposium on Multimedia, ISM 2016
CountryUnited States
CitySan Jose
Period12/11/1612/13/16

Keywords

  • Concept correlation
  • Imbalanced data
  • Information integration
  • Multimedia big data
  • Rare class mining

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Media Technology
  • Computer Science Applications

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  • Cite this

    Yan, Y., & Shyu, M-L. (2017). Enhancing rare class mining in multimedia big data by concept correlation. In Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016 (pp. 281-286). [7823629] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISM.2016.75