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Evolving Feature Selection
November/December 2005 (vol. 20 no. 6)
pp. 64-76
ASCII Text
x 
 
Huan Liu, Edward R. Dougherty, Jennifer G. Dy, Kari Torkkola, Eugene Tuv, Hanchuan Peng, Chris Ding, Fuhui Long, Michael Berens, Lance Parsons, Zheng Zhao, Lei Yu, George Forman, "Evolving Feature Selection," IEEE Intelligent Systems, vol. 20, no. 6, pp. 64-76, November/December, 2005.
 
 
BibTex
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@article{ 10.1109/MIS.2005.105,
author = {Huan Liu and Edward R. Dougherty and Jennifer G. Dy and Kari Torkkola and Eugene Tuv and Hanchuan Peng and Chris Ding and Fuhui Long and Michael Berens and Lance Parsons and Zheng Zhao and Lei Yu and George Forman},
title = {Evolving Feature Selection},
journal ={IEEE Intelligent Systems},
volume = {20},
number = {6},
issn = {1541-1672},
year = {2005},
pages = {64-76},
doi = {http://doi.ieeecomputersociety.org/10.1109/MIS.2005.105},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Intelligent Systems
TI - Evolving Feature Selection
IS - 6
SN - 1541-1672
SP64
EP76
EPD - 64-76
A1 - Huan Liu,
A1 - Edward R. Dougherty,
A1 - Jennifer G. Dy,
A1 - Kari Torkkola,
A1 - Eugene Tuv,
A1 - Hanchuan Peng,
A1 - Chris Ding,
A1 - Fuhui Long,
A1 - Michael Berens,
A1 - Lance Parsons,
A1 - Zheng Zhao,
A1 - Lei Yu,
A1 - George Forman,
PY - 2005
KW - feature selection
KW - data mining
KW - bioinformatics
KW - text mining
KW - clustering
KW - classification
VL - 20
JA - IEEE Intelligent Systems
ER -
 
Huan Liu, Arizona State University
Edward R. Dougherty, Texas A&M University
Jennifer G. Dy, Northeastern University
Kari Torkkola, Motorola
Eugene Tuv, Intel
Hanchuan Peng, Lawrence Berkeley National Laboratory
Chris Ding, Lawrence Berkeley National Laboratory
Fuhui Long, Lawrence Berkeley National Laboratory
Michael Berens, Translational Genomics Research Institute
Lance Parsons, Arizona State University
Zheng Zhao, Arizona State University
Lei Yu, State University of New York, Binghamton
George Forman, Hewlett-Packard Labs
Feature selection is a preprocessing technique, commonly used on high-dimensional data, that studies how to select a subset or list of attributes or variables that are used to construct models describing data. Wide data sets, which have a huge number of features but relatively few instances, introduce a novel challenge to feature selection. This installment of Trends & Controversies looks at several different ways of meeting this challenge.

This department is part of a special issue on Data Mining in Bioinformatics.

Index Terms:
feature selection, data mining, bioinformatics, text mining, clustering, classification
Citation:
Huan Liu, Edward R. Dougherty, Jennifer G. Dy, Kari Torkkola, Eugene Tuv, Hanchuan Peng, Chris Ding, Fuhui Long, Michael Berens, Lance Parsons, Zheng Zhao, Lei Yu, George Forman, "Evolving Feature Selection," IEEE Intelligent Systems, vol. 20, no. 6, pp. 64-76, Nov./Dec. 2005, doi:10.1109/MIS.2005.105
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