Feature Engineering for Machine Learning and Data Analytics

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.

1. Preliminaries and Overview Guozhu Dong and Huan Liu Preliminaries Overview of the Chapters Beyond this Book 2 Feature Engineering for Text Data Chase Geigle, Qiaozhu Mei, and ChengXiang Zhai Overview of Text Representation Text as Strings Sequence of Words Representation Bag of Words Representation Structural Representation of Text Latent Semantic Representation Explicit Semantic Representation Embeddings for Text Representation Context-Sensitive Text Representation 3 Feature Extraction and Learning for Visual Data Parag S. Chandakkar, Ragav Venkatesan, and Baoxin Li Classical Visual Feature Representations Latent-feature Extraction Deep Image Features 4 Feature-based time-series analysis Ben D. Fulcher Feature-based representations of time series Global features Subsequence features Combining time-series representations Feature-based forecasting 5 Feature Engineering for Data Streams Yao Ma, Jiliang Tang, and Charu Aggarwal Streaming Settings Linear Methods for Streaming Feature Construction Non-linear Methods for Streaming Feature Construction Feature Selection for Data Streams with Streaming Feature Feature Selection for Data Streams with Streaming Instances Discussions and Challenges 6 Feature Generation and Feature Engineering for Sequences Guozhu Dong, Lei Duan, Jyrki Nummenmaa, and Peng Zhang Basics on Sequence Data and Sequence Patterns Approaches to Using Patterns in Sequence Features Traditional Pattern-Based Sequence Features Mined Sequence Patterns for Use in Sequence Features Sequence Features Not De_ned by Patterns Sequence Databases 7 Feature Generation for Graphs and Networks Yuan Yao, Hanghang Tong, Feng Xu, and Jian Lu Feature Types Feature Generation . Feature Usages Future Directions 8 Feature Selection and Evaluation Yun Li and Tao LiFeature Selection Frameworks Advanced Topics for Feature Selection Future Work and Conclusion 9 Automating Feature Engineering in Supervised Learning Udayan Khurana A Few Simple Approaches Hierarchical Exploration of Feature Transformations Learning Optimal Traversal Policy Finding E_ective Features without Model Training Miscellenious 10 Pattern based Feature Generation Yunzhe Jia, James Bailey, Ramamohanarao Kotagiri, and Christopher Leckie Preliminaries Framework of pattern based feature generation Pattern mining algorithms Pattern selection approaches . Pattern based feature generation Pattern based feature generation for classi_cation Pattern based feature generation for clustering 11 Deep Learning for Feature Representation Suhang Wang and Huan Liu Restricted Boltzmann Machine AutoEncoder Convolutional Neural Networks Word Embedding and Recurrent Neural Networks . Generative Adversarial Networks and Variational Autoencoder Discussion and Further Readings 12 Feature Engineering for Social Bot Detection Onur Varol, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini Social bot detection . Online bot detection framework 13 Feature Generation and Engineering for Software Analytics Xin Xia and David Lo Features for Defect Prediction Features for Crash Release Prediction for Apps Features from Mining Monthly Reports to Predict Developer Turnover 14 Feature Engineering for Twitter-based Applications Sanjaya Wijeratne, Amit Sheth, Shrenyansh Bhatt, Lakshika Balasuriya, Hussein S. Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan

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 President's 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 http://www.public.asu.edu/~huanliu.