1st Edition
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 Li
Feature 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
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 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.
