Data Mining and Knowledge Discovery

, Volume 30, Issue 5, pp 1370–1394 | Cite as

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

  • Michiel Stock
  • Krzysztof Dembczyński
  • Bernard De Baets
  • Willem Waegeman
Article

Abstract

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-K predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach.

Keywords

Top-K retrieval Exact inference Precision at K Multi-target prediction 

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Michiel Stock
    • 1
  • Krzysztof Dembczyński
    • 2
  • Bernard De Baets
    • 1
  • Willem Waegeman
    • 1
  1. 1.KERMIT, Department of Mathematical Modelling, Statistics and BioinformaticsGhent UniversityGhentBelgium
  2. 2.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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