This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Feature Selection via Discretization
July-August 1997 (vol. 9 no. 4)
pp. 642-645
ASCII Text
x 
 
Huan Liu, Rudy Setiono, "Feature Selection via Discretization," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 4, pp. 642-645, July-August, 1997.
 
 
BibTex
x
 
@article{ 10.1109/69.617056,
author = {Huan Liu and Rudy Setiono},
title = {Feature Selection via Discretization},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {9},
number = {4},
issn = {1041-4347},
year = {1997},
pages = {642-645},
doi = {http://doi.ieeecomputersociety.org/10.1109/69.617056},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Feature Selection via Discretization
IS - 4
SN - 1041-4347
SP642
EP645
EPD - 642-645
A1 - Huan Liu,
A1 - Rudy Setiono,
PY - 1997
KW - Discretization
KW - feature selection
KW - pattern classification.
VL - 9
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 

Abstract—Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant and/or redundant attributes. Chi2 is a simple and general algorithm that uses the χ2 statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data. It achieves feature selection via discretization. It can handle mixed attributes, work with multiclass data, and remove irrelevant and redundant attributes.

[1] H. Almuallim and T.G. Dietterich, "Learning Boolean Concepts in the Presence of Many Irrelevant Features," Artificial Intelligence, vol. 69, nos. 1-2, pp. 279-305, Nov. 1994.
[2] J. Catlett, "On Changing Continuous Attributes into Ordered Discrete Attributes," European Working Session on Learning, 1991.
[3] U.M. Fayyad and K.B. Irani, "The Attribute Selection Problem in Decision Tree Generation," Proc. AAAI-92, Ninth Int'l Conf. Artificial Intelligence, pp. 104-110. AAAI Press/The MIT Press, 1992.
[4] R. Kerber, "ChiMerge: Discretization of Numeric Attributes," Proc. AAAI-92, Ninth Int'l Conf. Artificial Intelligence, pp. 123-128. AAAI Press/The MIT Press, 1992.
[5] K. Kira and L.A. Rendell, "The Feature Selection Problem: Traditional Methods and a New Algorithm," Proc. AAAI-92, Ninth Int'l Conf. Artificial Intelligence, pp. 129-134. AAAI Press/The MIT Press, 1992.
[6] H. Liu and W.X. Wen, "Concept Learning Through Feature Selection," Proc. First Australian and New Zealand Conf. Intelligent Information Systems, 1993.
[7] J. Murdoch and J.A. Barnes, Statistical Tables for Science, Engineering, Management, and Business Studies, MacMillan Press Ltd., 1986.
[8] J.R. Quinlan, C4.5: Programs for Machine Learning,San Mateo, Calif.: Morgan Kaufman, 1992.
[9] H. Ragavan and L. Rendell, "Lookahead Feature Construction for Learning Hard Concepts," Machine Learning: Proc. Seventh Int'l Conf., pp. 252-259.San Mateo, Calif.: Morgan Kaufmann, 1993.
[10] H. Liu and R. Setiono, "A Probabilistic Approach to Feature Selection—A Filter Solution," Proc. 13th Int'l Conf. Machine Learning, pp. 319-327, 1996.
[11] I. Sethi and G. Savarajudu, "Hierarchical Classifier Design Using Mutual Information," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 4, pp. 441-445, July 1982.
[12] N. Wyse, R. Dubes, and A.K. Jain, "A Critical Evaluation of Intrinsic Dimensionality Algorithms," E.S. Gelsema and L.N. Kanal, eds., Pattern Recognition in Practice, pp. 415-425.San Mateo, Calif.: Morgan Kaufmann, 1980.

Index Terms:
Discretization, feature selection, pattern classification.
Citation:
Huan Liu, Rudy Setiono, "Feature Selection via Discretization," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 4, pp. 642-645, July-Aug. 1997, doi:10.1109/69.617056
Usage of this product signifies your acceptance of the Terms of Use.