Over the last two decades, researchers are looking at imbalanced
data learning as a prominent research area. Many critical
real-world application areas like finance, health, network, news,
online advertisement, social network media, and weather have
imbalanced data, which emphasizes the research necessity for
real-time implications of precise fraud/defaulter detection, rare
disease/reaction prediction, network intrusion detection, fake news
detection, fraud advertisement detection, cyber bullying
identification, disaster events prediction, and more. Machine
learning algorithms are based on the heuristic of
equally-distributed balanced data and provide the biased result
towards the majority data class, which is not acceptable
considering imbalanced data is omnipresent in real-life scenarios
and is forcing us to learn from imbalanced data for foolproof
application design. Imbalanced data is multifaceted and demands a
new perception using the novelty at sampling approach of data
preprocessing, an active learning approach, and a cost perceptive
approach to resolve data imbalance. Data Preprocessing, Active
Learning, and Cost Perceptive Approaches for Resolving Data
Imbalance offers new aspects for imbalanced data learning by
providing the advancements of the traditional methods, with respect
to big data, through case studies and research from experts in
academia, engineering, and industry. The chapters provide
theoretical frameworks and the latest empirical research findings
that help to improve the understanding of the impact of imbalanced
data and its resolving techniques based on data preprocessing,
active learning, and cost perceptive approaches. This book is ideal
for data scientists, data analysts, engineers, practitioners,
researchers, academicians, and students looking for more
information on imbalanced data characteristics and solutions using
varied approaches.
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