Multilabel classification using error correction codes

Kouzani, Abbas Z. 2010, Multilabel classification using error correction codes, in Advances in computation and intelligence, Springer-Verlag, Heidelberg, Germany, pp.444-454.

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Title Multilabel classification using error correction codes
Author(s) Kouzani, Abbas Z.
Title of book Advances in computation and intelligence
Editor(s) Cai, Zhihua
Hu, Chengyu
Kang, Zhuo
Liu, Yong
Publication date 2010
Series Lecture notes in computer science; v.6382
Chapter number 45
Total chapters 53
Start page 444
End page 454
Total pages 11
Publisher Springer-Verlag
Place of Publication Heidelberg, Germany
Keyword(s) multilabel data
classification
error correction codes
ensemble learners
random forests
Summary This paper presents a multilabel classification method that employs an error correction code together with a base ensemble learner to deal with multilabel data. It explores two different error correction codes: convolutional code and BCH code. A random forest learner is used as its based learner. The performance of the proposed method is evaluated experimentally. The popular multilabel yeast dataset is used for benchmarking. The results are compared against those of several exiting approaches. The proposed method performs well against its counterparts.
ISBN 9783642164927
3642164927
ISSN 0302-9743
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 B1 Book chapter
HERDC collection year 2010
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032231

Document type: Book Chapter
Collection: School of Engineering
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Created: Thu, 13 Jan 2011, 16:07:39 EST by Abbas Kouzani

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