File(s) under permanent embargo
Multilabel classification using error correction codes
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
Title of book
Advances in computation and intelligenceSeries
Lecture notes in computer science; v.6382Chapter number
45Pagination
444 - 454Publisher
Springer-VerlagPlace of publication
Heidelberg, GermanyPublisher DOI
ISSN
0302-9743ISBN-13
9783642164927ISBN-10
3642164927Language
engPublication classification
B1 Book chapterCopyright notice
2010, SpringerExtent
53Editor/Contributor(s)
Z Cai, C Hu, Z Kang, Y LiuUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC