Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) '18
目次
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
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