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...
Κύριοι συγγραφείς: | , , , , , |
---|---|
Συγγραφή απο Οργανισμό/Αρχή: | |
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. .