A classifier ensemble framework for multimedia big data classification

Yilin Yan, Qiusha Zhu, Mei-Ling Shyu, Shu Ching Chen

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

14 Citations (Scopus)

Abstract

Numerous classification algorithms have been developed for a variety of data types. However, it is nearly impossible for one classifier to perform the best in all kinds of datasets. Therefore, ensemble learning models which aim to take advantages of different classifiers have received a lot of attentions recently. In this paper, a scalable classifier ensemble framework assisted by a set of judgers is proposed to integrate the outputs from multiple classifiers for multimedia big data classification. Specifically, based on the confusion matrices of different classifiers, a set of "judgers" are organized into a hierarchically structured decision model. A testing instance is first input to different classifiers, and then the classification results are passed to the proposed hierarchical structured decision model to derive the final result. The ensemble system can be run on Spark, which is designed for big data processing. Experimental results on multimedia data containing different actions demonstrate that the proposed classifier ensemble framework outperforms several state-of-The-Art model fusion approaches.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages615-622
Number of pages8
ISBN (Electronic)9781509032075
DOIs
StatePublished - 2016
Event17th IEEE International Conference on Information Reuse and Integration, IRI 2016 - Pittsburgh, United States
Duration: Jul 28 2016Jul 30 2016

Other

Other17th IEEE International Conference on Information Reuse and Integration, IRI 2016
CountryUnited States
CityPittsburgh
Period7/28/167/30/16

Fingerprint

Classifiers
Big data
Classifier
Multimedia
Electric sparks
Fusion reactions
Testing
Decision model

Keywords

  • Classifier Ensemble Framework
  • Ensemble learning
  • Multi-classifier Fusion
  • Multimedia Big Data
  • Spark

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

Cite this

Yan, Y., Zhu, Q., Shyu, M-L., & Chen, S. C. (2016). A classifier ensemble framework for multimedia big data classification. In Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016 (pp. 615-622). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IRI.2016.88

A classifier ensemble framework for multimedia big data classification. / Yan, Yilin; Zhu, Qiusha; Shyu, Mei-Ling; Chen, Shu Ching.

Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 615-622.

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

Yan, Y, Zhu, Q, Shyu, M-L & Chen, SC 2016, A classifier ensemble framework for multimedia big data classification. in Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016. Institute of Electrical and Electronics Engineers Inc., pp. 615-622, 17th IEEE International Conference on Information Reuse and Integration, IRI 2016, Pittsburgh, United States, 7/28/16. https://doi.org/10.1109/IRI.2016.88
Yan Y, Zhu Q, Shyu M-L, Chen SC. A classifier ensemble framework for multimedia big data classification. In Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 615-622 https://doi.org/10.1109/IRI.2016.88
Yan, Yilin ; Zhu, Qiusha ; Shyu, Mei-Ling ; Chen, Shu Ching. / A classifier ensemble framework for multimedia big data classification. Proceedings - 2016 IEEE 17th International Conference on Information Reuse and Integration, IRI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 615-622
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