Recent Advances in Feature Selection: A Data Perspective

author: Huan Liu, Department of Computer Science and Engineering, Arizona State University
author: Jundong Li, School of Computing, Informatics and Decision Systems Engineering, Arizona State University
author: Jiliang Tang, Department of Computer Science and Engineering, Michigan State University
published: Nov. 21, 2017,   recorded: August 2017,   views: 1103

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Feature selection, as a data preprocessing strategy, is imperative in preparing high-dimensional data for myriad of data mining and machine learning tasks. By selecting a subset of features of high quality, feature selection can help build simpler and more comprehensive models, improve data mining performance, and prepare clean and understandable data. The proliferation of big data in recent years has presented substantial challenges and opportunities for feature selection research. In this tutorial, we provide a comprehensive overview of recent advances in feature selection research from a data perspective. After we introduce some basic concepts, we review state-of-the-art feature selection algorithms and recent techniques of feature selection for structured, social, heterogeneous, and streaming data. In particular, we also discuss what the role of feature selection is in the context of deep learning and how feature selection is related to feature engineering. To facilitate and promote the research in this community, we present an open-source feature selection repository scikit-feature that consists of most of the popular feature selection algorithms. We conclude our discussion with some open problems and pressing issues in future research.

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