Rank Aggregation Algorithm Selection Meets Feature Selection

  • Alexey Zabashta
  • Ivan Smetannikov
  • Andrey Filchenkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9729)


Rank aggregation is the important task in many areas, and different rank aggregation algorithms are created to find optimal rank. Nevertheless, none of these algorithms is the best for all cases. The main goal of this work is to develop a method, which for each rank list defines, which rank aggregation algorithm is the best for this rank list. Canberra distance is used as a metric for determining the optimal ranking. Three approaches are proposed in this paper and one of them has shown promising result. Also we discuss, how this approach can be applied to learn filtering feature selection algorithm ensemble.


Meta-learning Rank aggregation Ensemble learning Feature selection 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alexey Zabashta
    • 1
  • Ivan Smetannikov
    • 1
  • Andrey Filchenkov
    • 1
  1. 1.Computer Science DepartmentITMO UniversitySt. PetersburgRussia

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