Challenges of Feature Selection for Big Data Analytics

被引：144

作者：

Li J. ^{[1
]}

Liu H. ^{[1
]}

机构：

[1] Li, Jundong

[2] Liu, Huan

来源：

| 1600年 / Institute of Electrical and Electronics Engineers Inc., United States卷 / 32期

基金：

美国国家科学基金会;

关键词：

big data; feature selection; intelligent systems; repository;

D O I：

10.1109/MIS.2017.38

中图分类号：

学科分类号：

摘要：

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. © 2017 IEEE.

引用

页码：9 / 15

页数：6

共 15 条

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