The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning
Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation.
The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry,
Imbalanced Learning: Foundations, Algorithms, and Applications
provides chapter coverage on:
Foundations of Imbalanced Learning
Imbalanced Datasets: From Sampling to Classifiers
Ensemble Methods for Class Imbalance Learning
Class Imbalance Learning Methods for Support Vector Machines
Class Imbalance and Active Learning
Nonstationary Stream Data Learning with Imbalanced Class Distribution
Assessment Metrics for Imbalanced Learning
will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.