Please use this identifier to cite or link to this item:
Title: Towards optimal discriminating order for multiclass classification
Authors: Liu, D.
Yan, S. 
Mu, Y. 
Hua, X.-S.
Chang, S.-F.
Zhang, H.-J.
Keywords: Discriminating order
Sequential Discriminating Tree
Issue Date: 2011
Citation: Liu, D.,Yan, S.,Mu, Y.,Hua, X.-S.,Chang, S.-F.,Zhang, H.-J. (2011). Towards optimal discriminating order for multiclass classification. Proceedings - IEEE International Conference on Data Mining, ICDM : 388-397. ScholarBank@NUS Repository.
Abstract: In this paper, we investigate how to design an optimized discriminating order for boosting multiclass classification. The main idea is to optimize a binary tree architecture, referred to as Sequential Discriminating Tree (SDT), that performs the multiclass classification through a hierarchical sequence of coarse-to-fine binary classifiers. To infer such a tree architecture, we employ the constrained large margin clustering procedure which enforces samples belonging to the same class to locate at the same side of the hyperplane while maximizing the margin between these two partitioned class subsets. The proposed SDT algorithm has a theoretic error bound which is shown experimentally to effectively guarantee the generalization performance. Experiment results indicate that SDT clearly beats the state-of-the-art multiclass classification algorithms. © 2011 IEEE.
Source Title: Proceedings - IEEE International Conference on Data Mining, ICDM
ISBN: 9780769544083
ISSN: 15504786
DOI: 10.1109/ICDM.2011.147
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Sep 10, 2019

Page view(s)

checked on Sep 8, 2019

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.