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Multilabel Classification Using Error Correction Codes

  • Abbas Z. Kouzani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6382)

Abstract

This paper presents a multilabel classification method that employs an error correction code together with a base ensemble learner to deal with multilabel data. It explores two different error correction codes: convolutional code and BCH code. A random forest learner is used as its based learner. The performance of the proposed method is evaluated experimentally. The popular multilabel yeast dataset is used for benchmarking. The results are compared against those of several exiting approaches. The proposed method performs well against its counterparts.

Keywords

Multilabel data classification error correction codes ensemble learners random forests 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Abbas Z. Kouzani
    • 1
  1. 1.School of EngineeringDeakin UniversityGeelongAustralia

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