Feature Selection-A Data Perspective

Feature Selection-A Data Perspective - Feature Selection A...

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arXiv:1601.07996v1 [cs.LG] 29 Jan 2016 Feature Selection: A Data Perspective Feature Selection: A Data Perspective Jundong Li [email protected] Arizona State University, Tempe, AZ 85281, USA Kewei Cheng [email protected] Arizona State University, Tempe, AZ 85281, USA Suhang Wang [email protected] Arizona State University, Tempe, AZ 85281, USA Fred Morstatter [email protected] Arizona State University, Tempe, AZ 85281, USA Robert P. Trevino [email protected] Arizona State University, Tempe, AZ 85281, USA Jiliang Tang [email protected] Yahoo! Labs, Sunnyvale, CA, 94085, USA Huan Liu [email protected] Arizona State University, Tempe, AZ 85281, USA Editor: Abstract Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing high-dimensional data for data mining and machine learning problems. The objectives of feature selection include: building simpler and more comprehensible models, improving data mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities of feature selection algorithms. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the big data age, we revisit feature selection research from a data perspective, and review representative feature selection algorithms for generic data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for generic data, we generally categorize them into four groups: similarity based, information theoretical based, sparse learning based and statistical based methods. Finally, to facilitate and promote the research in this community, we also present a open-source feature selection repository that consists of most of the popular feature selection algorithms ( ). At the end of this survey, we also have a discussion about some open problems and challenges that need to be paid more attention in future research. Keywords: Feature Selection 1
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Jundong Li et al. 1. Introduction We are now in the era of big data, where massive amounts of high dimensional data has become ubiquitous in our daily life, such as social media, e-commerce, health care, bioinformatics, transportation, online education, etc. Figure (1) shows an example by plotting the growth trend of UCI machine learning repository (Bache and Lichman, 2013). Rapid growth of data presents challenges for effective and efficient data management. Therefore, it is desirable and of great importance to apply data mining and machine learning techniques to automatically discover knowledge from these data.
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