kouzani-multilabelclassification-2009.pdf (370.67 kB)
Multilabel classification by BCH code and random forests
journal contribution
posted on 2009-11-01, 00:00 authored by Abbas KouzaniAbbas Kouzani, G NasiredingThis 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.
History
Journal
International journal of recent trends in engineeringVolume
2Issue
1Pagination
113 - 116Publisher
Academy PublisherLocation
Oulu, FinlandISSN
1797-9617Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2009, Academy PublisherUsage metrics
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