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Fourth IEEE International Conference on Data Mining (ICDM'04)
A Biobjective Model to Select Features with Good Classification Quality and Low Cost
Brighton, United Kingdom
November 01-November 04
ISBN: 0-7695-2142-8
ASCII Text
x 
 
Emilio Carrizosa, Belen Martin-Barragan, Dolores Romero Morales, "A Biobjective Model to Select Features with Good Classification Quality and Low Cost," Data Mining, IEEE International Conference on, pp. 339-342, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.
 
 
BibTex
x
 
@article{ 10.1109/ICDM.2004.10042,
author = {Emilio Carrizosa and Belen Martin-Barragan and Dolores Romero Morales},
title = {A Biobjective Model to Select Features with Good Classification Quality and Low Cost},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2004},
isbn = {0-7695-2142-8},
pages = {339-342},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10042},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - A Biobjective Model to Select Features with Good Classification Quality and Low Cost
SN - 0-7695-2142-8
SP339
EP342
A1 - Emilio Carrizosa,
A1 - Belen Martin-Barragan,
A1 - Dolores Romero Morales,
PY - 2004
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Emilio Carrizosa, Universidad de Sevilla (Spain)
Belen Martin-Barragan, Universidad de Sevilla (Spain)
Dolores Romero Morales, University of Oxford (United Kingdom)
In this paper we address a multi-group classification problem in which we want to take into account, together with the generalization ability, cots associated with the features. This cost is not limited to an economical payment, but can also refer to risk, computational effort, space requirements, etc. In order to get a good generalization ability, we use Support Vector Machines (SVM) as the basic mechanism by considering the maximization of the margin. We formulate the problem as a biobjective mixed integer problem, for which Pareto optimal solutions can be obtained.
Citation:
Emilio Carrizosa, Belen Martin-Barragan, Dolores Romero Morales, "A Biobjective Model to Select Features with Good Classification Quality and Low Cost," icdm, pp.339-342, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
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