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Multilabel classification using error correction codes

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posted on 2010-01-01, 00:00 authored by Abbas KouzaniAbbas Kouzani
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.

History

Chapter number

45

Pagination

444-454

ISSN

0302-9743

ISBN-13

9783642164927

ISBN-10

3642164927

Language

eng

Publication classification

B1 Book chapter

Copyright notice

2010, Springer

Extent

53

Editor/Contributor(s)

Cai Z, Hu C, Kang Z, Liu Y

Publisher

Springer-Verlag

Place of publication

Heidelberg, Germany

Title of book

Advances in computation and intelligence

Series

Lecture notes in computer science; v.6382

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