Paper Details

  • Title:

    On the combination of two decompositive multi-label classification methods

  • Author(s):

    Grigorios Tsoumakas, E. Loza Mencia, I. Katakis, S. Park, J. Furnkrnaz

  • Keywords: -
  • Abstract:

    In this paper, we compare and combine two approaches for multi-label classification that both decompose the initial problem into sets of smaller problems. The Calibrated Label Ranking approach is based on interpreting the multi-label problem as a preference learning problem and decomposes it into a quadratic number of binary classifiers. The HOMER approach reduces the original problem into a hierarchy of considerably simpler multi-label problems. Experimental results indicate that the use of HOMER is beneficial for the pairwise preference-based approach in terms of computational cost and quality of prediction.

  • Category: Conference Papers
  • Tags: 2009 Tsoumakas Loza Mencia Katakis Furnkrnaz