UID:
almahu_9949383696902882
Format:
1 online resource :
,
text file, PDF.
Edition:
First edition.
ISBN:
9781315181080
,
1315181088
,
9781351721271
,
1351721275
,
9781351721264
,
1351721267
Series Statement:
Chapman & Hall/CRC data mining and knowledge discovery series
Content:
"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."--Provided by publisher.
Note:
Part, I Feature Engineering for Various Data Types --
,
chapter 1 Preliminaries and overview /
,
chapter 2 Feature Engineering for Text Data /
,
chapter 3 Feature Extraction and Learning for Visual Data /
,
chapter 4 Feature-Based Time-Series Analysis /
,
chapter 5 Feature Engineering for Data Streams /
,
chapter 6 Feature Generation and Feature Engineering for Sequences /
,
chapter 7 Feature Generation for Graphs and Networks /
,
part, II General Feature Engineering Techniques --
,
chapter 8 Feature Selection and Evaluation /
,
chapter 9 Transformation-based Feature Engineering in Supervised Learning: Strategies toward Automation /
,
chapter 10 Pattern-Based Feature Generation /
,
chapter 11 Deep learning for feature representation /
,
part, III Feature Engineering in Special Applications --
,
chapter 12 Feature Engineering for Social Bot Detection /
,
chapter 13 Feature Generation and Engineering for Software Analytics /
,
chapter 14 Feature engineering for twitter-based applications /
Additional Edition:
ISBN 9781351721271
Additional Edition:
ISBN 9781351721264
Language:
English
Keywords:
Electronic books.
;
Electronic books.
URL:
https://www.taylorfrancis.com/books/9781315181080
Bookmarklink
