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Decision Trees and Their Families in Imbalanced Pattern Recognition: Recognition with and without Rejection

  • Wladyslaw Homenda
  • Wojciech Lesinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8838)

Abstract

Decision trees are considered to be among the best classifiers. In this work we use decision trees and its families to the problem of imbalanced data recognition. Considered are aspects of recognition without rejection and with rejection: it is assumed that all recognized elements belong to desired classes in the first case and that some of them are outside of such classes and are not known at classifier’s training stage. The facets of imbalanced data and recognition with rejection affect different real world problems. In this paper we discuss results of experiment of imbalanced data recognition on the case study of music notation symbols. Decision trees and three methods of joining decision trees (simple voting, bagging and random forest) are studied. These methods are used for recognition without and with rejection.

Keywords

pattern recognition decision tree bagging random forest optical music recognition imbalanced data 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Wladyslaw Homenda
    • 1
  • Wojciech Lesinski
    • 2
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  2. 2.Faculty of Mathematics and Computer ScienceUniversity of BialystokBialystokPoland

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