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An Ensemble Learning Approach Based on Missing-Valued Tables

  • Seiki UbukataEmail author
  • Taro Miyazaki
  • Akira Notsu
  • Katsuhiro Honda
  • Masahiro Inuiguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)

Abstract

In classification problems on rough sets, the effectiveness of ensemble learning approaches such as bagging, random forests, and attribute sampling ensemble has been reported. We focus on occurrences of deficiencies in columns on the original decision table in random forests and attribute sampling ensemble approaches. In this paper, we generalize such deficiencies of columns to deficiencies of cells and propose an ensemble learning approach based on missing-valued decision tables. We confirmed the effectiveness of the proposed method for the classification performance through numerical experiments and the two-tailed Wilcoxon signed-rank test. Furthermore, we consider the robustness of the method in absences of condition attribute values of unknown objects.

Keywords

Rough sets Ensemble learning MLEM2 Incomplete data Missing attribute values 

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Seiki Ubukata
    • 1
    Email author
  • Taro Miyazaki
    • 2
  • Akira Notsu
    • 1
  • Katsuhiro Honda
    • 1
  • Masahiro Inuiguchi
    • 2
  1. 1.Graduate School of Engineering, Osaka Prefecture UniversitySakaiJapan
  2. 2.Graduate School of Engineering Science, Osaka UniversityToyonakaJapan

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