Challenges of Feature Selection for Big Data Analytics

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.

• Publication year

  2016

• Venue

  IEEE Intelligent Systems

• Publication date

  2016-11-07

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/MIS.2017.38arXiv 1611.01875
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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