Description
Book SynopsisFeature 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-specif
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
