Document Type

Article

Language

eng

Publication Date

11-2006

Publisher

Institute of Electrical and Electronic Engineers (IEEE)

Source Publication

IEEE Transactions on Neural Networks

Source ISSN

1045-9227

Abstract

Using dynamic programming, this work develops a one-class-at-a-time removal sequence planning method to decompose a multiclass classification problem into a series of two-class problems. Compared with previous decomposition methods, the approach has the following distinct features. First, under the one-class-at-a-time framework, the approach guarantees the optimality of the decomposition. Second, for a K-class problem, the number of binary classifiers required by the method is only K-1. Third, to achieve higher classification accuracy, the approach can easily be adapted to form a committee machine. A drawback of the approach is that its computational burden increases rapidly with the number of classes. To resolve this difficulty, a partial decomposition technique is introduced that reduces the computational cost by generating a suboptimal solution. Experimental results demonstrate that the proposed approach consistently outperforms two conventional decomposition methods.

Comments

Accepted version. IEEE Transactions on Neural Networks, Vol. 17, No. 6, (November 2006): 1544-1549. DOI. © 2006 IEEE. Used with permission.

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