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Combining Dimensionality Reduction with Random Forests for Multi-label Classification Under Interactivity Constraints

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

Learning from multi-label data in an interactive framework is a challenging problem as algorithms must withstand some additional constraints: in particular, learning from few training examples in a limited time. A recent study of multi-label classifier behaviors in this context has identified the potential of the ensemble method “Random Forest of Predictive Clustering Trees” (RF-PCT). However, RF-PCT has shown a degraded performance in terms of computation time for large feature spaces. To overcome this limit, this paper proposes a new hybrid multi-label learning approach IDSR-RF (Independent Dual Space Reduction with RF-PCT) which first reduces the data dimension and then learns a predictive regression model in the reduced spaces with RF-PCT. The feature and the label spaces are independently reduced using the fast matrix factorization algorithm Gravity. The experimental results on nine high-dimensional datasets show that IDSR-RF significantly reduces the computation time without deteriorating the learning performances. To the best of our knowledge, it is currently the most promising learning approach for an interactive multi-label learning system.

Keywords

Root Mean Square Error Unlabelled Data Interactive Framework Label Space Label Ranking 
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 International Publishing AG 2017

Authors and Affiliations

  1. 1.TechnicolorCesson-sévigné, RennesFrance
  2. 2.Laboratoire d’Informatique de Nantes AtlantiqueSite Polytech’NantesNantes CedexFrance
  3. 3.Orange LabsLannion CedexFrance

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