One-class-at-a-time removal sequence planning method for multiclass classification problems

Chieh Neng Young, Chen Wen Yen, Yi Hua Pao, Mark L. Nagurka

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)1544-1549
Number of pages6
JournalIEEE Transactions on Neural Networks
Volume17
Issue number6
DOIs
StatePublished - 1 Nov 2006
Externally publishedYes

Keywords

  • Dynamic programming
  • Multiclass classification
  • Pattern recognition

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