Institute of Electrical and Electronic Engineers (IEEE)
IEEE Transactions on Neural Networks
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.
Young, Chieh-Neng; Yen, Chen-Wen; Pao, Yi-Hua; and Nagurka, Mark L., "One-Class-at-a-Time Removal Sequence Planning Method for Multiclass Classification Problems" (2006). Mechanical Engineering Faculty Research and Publications. 198.
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