This paper presents a dual-random ensemble multi-label classification method for classification of multi-label data. The method is formed by integrating and extending the concepts of feature subspace method and random k-label set ensemble multi-label classification method. Experiemental results show that the developed method outperforms the exisiting multi-lable classification methods on three different multi-lable datasets including the biological yeast and genbase datasets.
History
Event
Bioelectronics and Bioinformatics. Symposium (2009 : Melbourme, Victoria)
Pagination
49 - 52
Publisher
ISBB
Location
Melbourne, Victoria
Place of publication
Melbourne, Vic.
Start date
2009-12-09
End date
2009-12-11
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2009, ISBB
Title of proceedings
ISBB 2009 : Proceedings of the 2009 International Symposium on Bioelectronics and Bioinformatics