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Multilabel classification by BCH code and random forests

Kouzani, Abbas Z. and Nasireding, Gulisong 2009, Multilabel classification by BCH code and random forests, International journal of recent trends in engineering, vol. 2, no. 1, pp. 113-116.

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Title Multilabel classification by BCH code and random forests
Author(s) Kouzani, Abbas Z.
Nasireding, Gulisong
Journal name International journal of recent trends in engineering
Volume number 2
Issue number 1
Start page 113
End page 116
Total pages 4
Publisher Academy Publisher
Place of publication Oulu, Finland
Publication date 2009-11
ISSN 1797-9617
Keyword(s) multilabel data
multilabel classification
BCH code
ensemble learners
Summary This paper uses error correcting codes for multilabel classification. BCH code and random forests learner are used to form the proposed method. Thus, the advantage of the error-correcting properties of BCH is merged with the good performance of the random forests learner to enhance the multilabel classification results. Three experiments are conducted on three common benchmark datasets. The results are compared against those of several exiting approaches. The proposed method does well against its counterparts for the three datasets of varying characteristics.
Language eng
Field of Research 090609 Signal Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890301 Electronic Information Storage and Retrieval Services
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2009, Academy Publisher
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028670

Document type: Journal Article
Collections: School of Engineering
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Created: Tue, 25 May 2010, 13:03:55 EST by Abbas Kouzani

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.