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Ensembles of Nested Dichotomies with Multiple Subset Evaluation

  • Tim LeathartEmail author
  • Eibe Frank
  • Bernhard Pfahringer
  • Geoffrey Holmes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)

Abstract

A system of nested dichotomies (NDs) is a method of decomposing a multiclass problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each with various advantages and disadvantages. In this paper, we present a simple, general method for improving the predictive performance of NDs produced by any subset selection techniques that employ randomness to construct the subsets. We provide a theoretical expectation for performance improvements, as well as empirical results showing that our method improves the root mean squared error of NDs, regardless of whether they are employed as an individual model or in an ensemble setting.

Supplementary material

482290_1_En_7_MOESM1_ESM.pdf (40 kb)
Supplementary material 1 (pdf 39 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tim Leathart
    • 1
    Email author
  • Eibe Frank
    • 1
  • Bernhard Pfahringer
    • 1
  • Geoffrey Holmes
    • 1
  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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