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A triple-random ensemble classification method for mining multi-label data

Nasierding, Gulisong, Kouzani, Abbas Z. and Tsoumakas, Grigorios 2010, A triple-random ensemble classification method for mining multi-label data, in ICDMW 2010 : Proceedings of 10th IEEE International Conference on Data Mining Workshops, IEEE Computer Society, Sydney, NSW, pp. 49-56.

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Title A triple-random ensemble classification method for mining multi-label data
Author(s) Nasierding, Gulisong
Kouzani, Abbas Z.
Tsoumakas, Grigorios
Conference name International Conference on Data Mining Workshops (10th : 2010 : Sydney, N.S.W.)
Conference location Sydney, NSW
Conference dates 14 Dec. 2010
Title of proceedings ICDMW 2010 : Proceedings of 10th IEEE International Conference on Data Mining Workshops
Editor(s) Fan, Wei
Hsu, Wynne
Webb, Geoffrey I.
Liu, Bing
Zhang, Chengqi
Gunopulos, Dimitrios
Wu, Xindong
Publication date 2010
Conference series International Conference on Data Mining
Start page 49
End page 56
Total pages 8
Publisher IEEE Computer Society
Place of publication Sydney, NSW
Keyword(s) triple-random ensemble
multi-label classification
subspace method
RAkEL
bagging
Summary This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.
ISBN 9780769542577
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 E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30032345

Document type: Conference Paper
Collections: School of Engineering
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Created: Tue, 25 Jan 2011, 08:40:44 EST by Abbas Kouzani

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