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Issue No. 02 - Mar.-Apr. (2017 vol. 32)
ISSN: 1541-1672
pp: 9-15
Jundong Li , Arizona State University
Huan Liu , Arizona State University
We're surrounded by huge amounts of large-scale high-dimensional data, but learning tasks require reduced data dimensionality. Feature selection has shown its effectiveness in many applications by building simpler and more comprehensive models, improving learning performance, and preparing clean, understandable data. Some unique characteristics of big data such as data velocity and data variety have presented challenges to the feature selection problem. In this article, the authors envision these challenges for big data analytics. To facilitate and promote feature selection research, they present an open source feature selection repository (scikit-feature) of popular algorithms.
Big Data, Algorithm design and analysis, Filtering algorithms, Bioinformatics, Data mining, Clustering algorithms, Feature extraction

J. Li and H. Liu, "Challenges of Feature Selection for Big Data Analytics," in IEEE Intelligent Systems, vol. 32, no. 2, pp. 9-15, 2017.
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