Imbalanced Learning (PDF)

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

Imbalanced Learning: Foundations, Algorithms, and
Applications 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.