Effective feature space reduction with imbalanced data for semantic concept detection

Lin Lin, Guy Ravitz, Mei-Ling Shyu, Shu Ching Chen

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

32 Citations (Scopus)

Abstract

Semantic understanding of multimedia content has become a very popular research topic in recent years. Semantic concept detection algorithms face many challenges such as the semantic gap and imbalance data, among others. In this paper, we propose a novel algorithm using multiple correspondence analysis (MCA) to discover the correlation between features and classes to reduce the feature space and to bridge the semantic gap. Moreover, the proposed algorithm is able to explore the correlation between items (i.e., feature-value pairs generated for each of the features) and classes which expands its ability to handle imbalance data sets. To evaluate the proposed algorithm, we compare its performance on semantic concept detection with several existing feature selection methods under various well-known classifiers using some of the concepts and benchmark data available from the TRECVID project. The results demonstrate that our proposed algorithm achieves promising performance, and it performs significantly better than those feature selection methods in the comparison for the imbalanced data sets.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Pages262-269
Number of pages8
DOIs
StatePublished - Sep 9 2008
Event2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008 - Taichung, Taiwan, Province of China
Duration: Jun 11 2008Jun 13 2008

Other

Other2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008
CountryTaiwan, Province of China
CityTaichung
Period6/11/086/13/08

Fingerprint

Semantics
Feature extraction
Classifiers

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Lin, L., Ravitz, G., Shyu, M-L., & Chen, S. C. (2008). Effective feature space reduction with imbalanced data for semantic concept detection. In Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (pp. 262-269). [4545766] https://doi.org/10.1109/SUTC.2008.66

Effective feature space reduction with imbalanced data for semantic concept detection. / Lin, Lin; Ravitz, Guy; Shyu, Mei-Ling; Chen, Shu Ching.

Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. 2008. p. 262-269 4545766.

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

Lin, L, Ravitz, G, Shyu, M-L & Chen, SC 2008, Effective feature space reduction with imbalanced data for semantic concept detection. in Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing., 4545766, pp. 262-269, 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, SUTC 2008, Taichung, Taiwan, Province of China, 6/11/08. https://doi.org/10.1109/SUTC.2008.66
Lin L, Ravitz G, Shyu M-L, Chen SC. Effective feature space reduction with imbalanced data for semantic concept detection. In Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. 2008. p. 262-269. 4545766 https://doi.org/10.1109/SUTC.2008.66
Lin, Lin ; Ravitz, Guy ; Shyu, Mei-Ling ; Chen, Shu Ching. / Effective feature space reduction with imbalanced data for semantic concept detection. Proceedings - IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing. 2008. pp. 262-269
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