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Optimal monotone relabelling of partially non-monotone ordinal data

Michaël Rademaker UGent, Bernard De Baets UGent and Hans De Meyer UGent (2012) OPTIMIZATION METHODS & SOFTWARE. 27(1). p.17-31
abstract
Noise in multi-criteria data sets can manifest itself as non-monotonicity. Work on the remediation of such non-monotonicity is rather scarce. Nevertheless, errors are often present in real-life data sets, and several monotone classification algorithms are unable to use such partially non-monotone data sets. Fortunately, as we will show here, it is possible to restore monotonicity in an optimal way, by relabelling part of the data set. By exploiting the properties of a (minimum) flow network, and identifying pleasing properties of some maximum cuts, an elegant single-pass optimal ordinal relabelling algorithm is formulated.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
monotonicity, ordinal data, monotone relabelling, flow network, maximum independent set, maximum cut, AGGREGATION OPERATORS, RANKING, SETS
journal title
OPTIMIZATION METHODS & SOFTWARE
Optim. Method Softw.
volume
27
issue
1
pages
17 - 31
Web of Science type
Article
Web of Science id
000302315500002
JCR category
MATHEMATICS, APPLIED
JCR impact factor
0.683 (2012)
JCR rank
143/247 (2012)
JCR quartile
3 (2012)
ISSN
1055-6788
DOI
10.1080/10556788.2010.507272
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2918678
handle
http://hdl.handle.net/1854/LU-2918678
date created
2012-06-25 15:01:31
date last changed
2012-07-06 12:02:07
@article{2918678,
  abstract     = {Noise in multi-criteria data sets can manifest itself as non-monotonicity. Work on the remediation of such non-monotonicity is rather scarce. Nevertheless, errors are often present in real-life data sets, and several monotone classification algorithms are unable to use such partially non-monotone data sets. Fortunately, as we will show here, it is possible to restore monotonicity in an optimal way, by relabelling part of the data set. By exploiting the properties of a (minimum) flow network, and identifying pleasing properties of some maximum cuts, an elegant single-pass optimal ordinal relabelling algorithm is formulated.},
  author       = {Rademaker, Micha{\"e}l and De Baets, Bernard and De Meyer, Hans},
  issn         = {1055-6788},
  journal      = {OPTIMIZATION METHODS \& SOFTWARE},
  keyword      = {monotonicity,ordinal data,monotone relabelling,flow network,maximum independent set,maximum cut,AGGREGATION OPERATORS,RANKING,SETS},
  language     = {eng},
  number       = {1},
  pages        = {17--31},
  title        = {Optimal monotone relabelling of partially non-monotone ordinal data},
  url          = {http://dx.doi.org/10.1080/10556788.2010.507272},
  volume       = {27},
  year         = {2012},
}

Chicago
Rademaker, Michaël, Bernard De Baets, and Hans De Meyer. 2012. “Optimal Monotone Relabelling of Partially Non-monotone Ordinal Data.” Optimization Methods & Software 27 (1): 17–31.
APA
Rademaker, M., De Baets, B., & De Meyer, H. (2012). Optimal monotone relabelling of partially non-monotone ordinal data. OPTIMIZATION METHODS & SOFTWARE, 27(1), 17–31.
Vancouver
1.
Rademaker M, De Baets B, De Meyer H. Optimal monotone relabelling of partially non-monotone ordinal data. OPTIMIZATION METHODS & SOFTWARE. 2012;27(1):17–31.
MLA
Rademaker, Michaël, Bernard De Baets, and Hans De Meyer. “Optimal Monotone Relabelling of Partially Non-monotone Ordinal Data.” OPTIMIZATION METHODS & SOFTWARE 27.1 (2012): 17–31. Print.