A Study on Multi-label Classification

  • Clifford A. Tawiah
  • Victor S. Sheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7987)


Multi-label classifications exist in many real world applications. This paper empirically studies the performance of a variety of multi-label classification algorithms. Some of them are developed based on problem transformation. Some of them are developed based on adaption. Our experimental results show that the adaptive Multi-Label K-Nearest Neighbor performs the best, followed by Random k-Label Set, followed by Classifier Chain and Binary Relevance. Adaboost.MH performs the worst, followed by Pruned Problem Transformation. Our experimental results also provide us the confidence of existing correlations among multi-labels. These insights shed light for future research directions on multi-label classifications.


multi-label classification Multi-Label K-Nearest Neighbor Random k-Label Set Adaboost.MH Classifier Chain Binary Relevance Pruned Problem Transformation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Clifford A. Tawiah
    • 1
  • Victor S. Sheng
    • 1
  1. 1.Department of Computer ScienceUniversity of Central ArkansasConwayUSA

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