Multi-label classification with clustering for image and text categorization

Nasierding, Gulisong and Sajjanhar, Atul 2013, Multi-label classification with clustering for image and text categorization, in Proceedings of the 6th International Congress on Image and Signal Processing; CISP 2013, IEEE, Piscataway, NJ, pp. 869-874.

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Title Multi-label classification with clustering for image and text categorization
Author(s) Nasierding, Gulisong
Sajjanhar, Atul
Conference name International Congress on Image and Signal Processing (6th : 2013 : Hangzhou, China)
Conference location Hangzhou, China
Conference dates 16-18 Dec. 2013
Title of proceedings Proceedings of the 6th International Congress on Image and Signal Processing; CISP 2013
Editor(s) [Unknown]
Publication date 2013
Conference series International Congress on Image and Signal Processing
Start page 869
End page 874
Total pages 6
Publisher IEEE
Place of publication Piscataway, NJ
Keyword(s) multi-label classification
clustering
image and text categorization
Summary This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.
ISBN 9781479927647
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060771

Document type: Conference Paper
Collection: School of Information Technology
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