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Selecting a Multi-Label Classification Method for an Interactive System

  • Noureddine-Yassine NAIR-BENREKIAEmail author
  • Pascale Kuntz
  • Frank Meyer
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Interactive classification-based systems engage users to coach learning algorithms to take into account their own individual preferences. However most of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classification methods in an interactive framework where “good” predictions must be produced in a very short time from a very small set of multi-label training examples. Experimentations highlight important performance differences for four complementary evaluation measures (Log-Loss, Ranking-Loss, Learning and Prediction Times). The best results are obtained for Multi-label k Nearest Neighbors (ML-kNN), ensemble of classifier chains (ECC), and ensemble of binary relevance (EBR).

Keywords

Ensemble Method Training Subset Binary Relevance Classifier Chain Relevant Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Noureddine-Yassine NAIR-BENREKIA
    • 1
    • 2
    Email author
  • Pascale Kuntz
    • 2
  • Frank Meyer
    • 1
  1. 1.Orange LabsLannion cedexFrance
  2. 2.LINANANTES cedexFrance

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