Data Intrinsic Characteristics

  • Alberto Fernández
  • Salvador García
  • Mikel Galar
  • Ronaldo C. Prati
  • Bartosz Krawczyk
  • Francisco Herrera


Although class imbalance is often pointed out as a determinant factor for degradation in classification performance, there are situations in which good performance can be achieve even in the presence of severe class imbalance. The identification of situation where the class imbalance is a complicating factor is an important research question. These situations are often associated to some data intrinsic characteristics. This chapter describes some of these characteristics. Section 10.2 discuss some studies using data complexity measures for categorizing imbalanced datasets. Section 10.3 discuss the relationship between class imbalance and small disjuncts. Section 10.4 analyses the problem of data rarity or lack of data. Section 10.5 discuss the problem of class overlapping, a complicating factor for class imbalance. Section 10.6 discuss the problem of noise in the context of class imbalance. The influence of borderline instances is discussed in Sect. 10.7. Section 10.8 analyses the problem on shifting between training and deployment datasets. Section 10.9 describes problems with imperfect data. Finally, Sect. 10.10 concludes this chapter.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alberto Fernández
    • 1
  • Salvador García
    • 1
  • Mikel Galar
    • 2
  • Ronaldo C. Prati
    • 3
  • Bartosz Krawczyk
    • 4
  • Francisco Herrera
    • 5
  1. 1.Department of Computer Science and AIUniversity of GranadaGranadaSpain
  2. 2.Institute of Smart CitiesPublic University of NavarrePamplonaSpain
  3. 3.Department of Computer ScienceUniversidade Federal do ABCSanto AndreBrazil
  4. 4.Department of Computer ScienceVirginia Commonwealth UniversityRichmondUSA
  5. 5.Department of Computer Science and AIUniversity of GranadaGranadaSpain

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