Learning from Imbalanced Data Sets

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced clas...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Fernández, Alberto (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), García, Salvador (http://id.loc.gov/vocabulary/relators/aut), Galar, Mikel (http://id.loc.gov/vocabulary/relators/aut), Prati, Ronaldo C. (http://id.loc.gov/vocabulary/relators/aut), Krawczyk, Bartosz (http://id.loc.gov/vocabulary/relators/aut), Herrera, Francisco (http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • 1 Introduction to KDD and Data Science
  • 2 Foundations on Imbalanced Classification
  • 3 Performance measures
  • 4 Cost-sensitive Learning
  • 5 Data Level Preprocessing Methods
  • 6 Algorithm-level Approaches
  • 7 Ensemble Learning
  • 8 Imbalanced Classification with Multiple Classes
  • 9 Dimensionality Reduction for Imbalanced Learning
  • 10 Data Intrinsic Characteristics
  • 11 Learning from Imbalanced Data Streams
  • 12 Non-Classical Imbalanced Classification Problems
  • 13 Imbalanced Classification for Big Data
  • 14 Software and Libraries for Imbalanced Classification. .