Learning from Imbalanced Data Sets

• Main Authors: Fernández, Alberto (Author, 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)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Subjects: Artificial intelligence.; Computers.; Computer communication systems.; Artificial Intelligence.; Information Systems and Communication Service.; Computer Communication Networks.

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...

Table of Contents:

• 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. .