Imbalanced Learning: Foundations, Algorithms, and Applications Foundations, Algorithms, and Applications

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.

This book focuses on the imbalanced learning (i.e., learning from imbalanced data) topic, a critical emerging research topic in many of today's data-intensive complex, and networked systems, such as surveillance, security, Internet, finance, biomedical, defense, and many others. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. 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. This book aims to provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of thethe problem, the state-of-the-art technologies, the critical application domains, as well as major opportunities and important future research directions.