Feature Engineering for Machine Learning and Data Analytics by Huan Liu (English

Description: FREE SHIPPING UK WIDE Feature Engineering for Machine Learning and Data Analytics by Huan Liu, Guozhu Dong Edited by two of the leading experts in the field, this book provides a comprehensive reference book on feature engineering. The book provides a description of problems and applications for feature engineering, as well as its techniques, principles, issues, and challenges. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Author Biography Dr. Guozhu Dong is a professor of Computer Science and Engineering at Wright State University. He obtained his Ph.D. in Computer Science from University of Southern California and his B.S. in Mathematics from Shandong University. Before joining Wright State University, he was a faculty member at Flinders University and then at the University of Melbourne. At Wright State University, he was recognized for Excellence in Research in the College of Engineering and Computer Science. His research interests are in data mining, machine learning, database, data science, and artificial intelligence. He co-authored a book on Sequence Data Mining and co-edited a book on Contrast Data Mining. He has served on numerous conference program committees.Dr. Huan Liu is a professor of Computer Science and Engineering at Arizona State University. He obtained his Ph.D. in Computer Science at University of Southern California and B.Eng. in Computer Science and Electrical Engineering at Shanghai JiaoTong University. Before he joined ASU, he worked at Telecom Australia Research Labs and was on the faculty at National University of Singapore. At Arizona State University, he was recognized for excellence in teaching and research in Computer Science and Engineering and received the 2014 Presidents Award for Innovation. His research interests are in data mining, machine learning, social computing, and artificial intelligence, investigating interdisciplinary problems that arise in many real-world, data-intensive applications with high-dimensional data of disparate forms such as social media. His well-cited publications include books, book chapters, encyclopedia entries as well as conference and journal papers. He is a co-author of Social Media Mining: An Introduction by Cambridge University Press. He serves on journal editorial boards and numerous conference program committees, and is a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is an IEEE Fellow. More can be found at /~huanliu. Table of Contents 1. Preliminaries and Overview 2. Feature Engineering for Text Data 3. Feature Extraction and Learning for Visual Data 4. Feature-based time-series analysis 5. Feature Engineering for Data Streams 6. Feature Generation and Feature Engineering for Sequences 7. Feature Generation for Graphs and Networks 8. Feature Selection and Evaluation 9. Automating Feature Engineering in Supervised Learning 10. Pattern based Feature Generation 11. Deep Learning for Feature Representation 12. Feature Engineering for Social Bot Detection 13. Feature Generation and Engineering for Software Analytics 14. Feature Engineering for Twitter-based Applications Details ISBN1138744387 Publisher Taylor & Francis Ltd Year 2018 ISBN-10 1138744387 ISBN-13 9781138744387 Format Hardcover Imprint CRC Press Place of Publication London Country of Publication United Kingdom Edited by Huan Liu Affiliation Arizona State University, Arizona, USA DEWEY 006.312 Pages 400 Language English Illustrations 40 Tables, black and white; 76 Illustrations, black and white AU Release Date 2018-04-04 NZ Release Date 2018-04-04 Author Guozhu Dong Publication Date 2018-04-04 UK Release Date 2018-04-04 Series Chapman & Hall/CRC Data Mining and Knowledge Discovery Series Alternative 9780367571856 Audience Tertiary & Higher Education We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 30 DAY RETURN POLICY No questions asked, 30 day returns! 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