Imbalance Data Classification via Neural-Like Structures of Geometric Transformations Model: Local and Global Approaches

  • Roman Tkachenko
  • Anastasiya Doroshenko
  • Ivan Izonin
  • Yurii Tsymbal
  • Bohdana Havrysh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

The classification task is one of the most widespread among the tasks of Data Mining - spam detection, medical diagnosis, ad targeting, risk assessment and image classification. However, all these tasks have a common feature - training dataset can be unbalanced, the number of instances of the target class can be less than one percent of all data. In this article, we compare the results of solving one of these problems using the most common classification methods (Random Forest Leaner, Logistic Regression, SVM). The article describes a new classification method based on neural-like structures of Geometric Transformations Model (local and global approaches) and compares their result with the obtained results.

Keywords

Classification Imbalance data Neural-like structures  Data unevenness Geometric transformations model Machine learning 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Roman Tkachenko
    • 1
  • Anastasiya Doroshenko
    • 1
  • Ivan Izonin
    • 1
  • Yurii Tsymbal
    • 1
  • Bohdana Havrysh
    • 2
  1. 1.Lviv Polytechnic National UniversityLvivUkraine
  2. 2.Ukrainian Graphic Arts AcademyLvivUkraine

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