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|Title:||Towards optimal discriminating order for multiclass classification||Authors:||Liu, D.
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. https://doi.org/10.1109/ICDM.2011.147||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/72056||ISBN:||9780769544083||ISSN:||15504786||DOI:||10.1109/ICDM.2011.147|
|Appears in Collections:||Staff Publications|
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