Author(s) Name:  Guozhu Dong, Huan Liu

About the Book:

   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.

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

ISBN:  9780367571856

Publisher:  CRC Press Publisher

Year of Publication:  2018

Book Link:  Home Page Url