Feature engineering plays a vital role in big data analytics.
Machine learning and data mining algorithms cannot work without
data. Little can be achieved if there are few features to represent
the underlying data objects, and the quality of results of those
algorithms largely depends on the quality of the available
features. Feature Engineering for Machine Learning and Data
Analytics provides a comprehensive introduction to feature
engineering, including feature generation, feature extraction,
feature transformation, feature selection, and feature analysis and
evaluation. The book presents key concepts, methods, examples, and
applications, as well as chapters on feature engineering for major
data types such as texts, images, sequences, time series, graphs,
streaming data, software engineering data, Twitter data, and social
media data. It also contains generic feature generation approaches,
as well as methods for generating tried-and-tested, hand-crafted,
domain-specific features. The first chapter defines the concepts of
features and feature engineering, offers an overview of the book,
and provides pointers to topics not covered in this book. The next
six chapters are devoted to feature engineering, including feature
generation for specific data types. The subsequent four chapters
cover generic approaches for feature engineering, namely feature
selection, feature transformation based feature engineering, deep
learning based feature engineering, and pattern based feature
generation and engineering. The last three chapters discuss feature
engineering for social bot detection, software management, and
Twitter-based applications respectively. This book can be used as a
reference for data analysts, big data scientists, data
preprocessing workers, project managers, project developers,
prediction modelers, professors, researchers, graduate students,
and upper level undergraduate students. It can also be used as the
primary text for courses on feature engineering, or as a supplement
for courses on machine learning, data mining, and big data
analytics.
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