An improved multi-label classification based on label ranking and delicate boundary SVM

  • Chen B
  • Gu W
  • Hu J
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In this paper, an improved multi-label classification is proposed
based on label ranking and delicate decision boundary SVM. Firstly,
an improved probabilistic SVM with delicate decision boundary is
used as the scoring method to obtain a proper label rank. It can
improve the probabilistic label rank by introducing the information
of overlapped training samples into learning procedure. Secondly,
a threshold selection related with input instance and label rank
is proposed to decide the classification results. It can estimate
an appropriate threshold for each testing instance according to the
characteristics of instance and label rank. Experimental results
on four multi-label benchmark datasets show that the proposed method
improves the performance of classification efficiently, compared
with binary SVM method and some existing well-known methods.

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  • Benhui Chen

  • Weifeng Gu

  • Jinglu Hu

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