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Rank Aggregation Algorithm Selection Meets Feature Selection

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

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.

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Authors and Affiliations

  1. Computer Science Department, ITMO University, 49 Kronverksky Pr., 197101, St. Petersburg, Russia

    Alexey Zabashta, Ivan Smetannikov & Andrey Filchenkov

Authors
  1. Alexey Zabashta
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  2. Ivan Smetannikov
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  3. Andrey Filchenkov
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Corresponding author

Correspondence to Ivan Smetannikov .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Zabashta, A., Smetannikov, I., Filchenkov, A. (2016). Rank Aggregation Algorithm Selection Meets Feature Selection. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_56

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

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