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26th Annual International Computer Software and Applications Conference
Mining Decision-Rule Preference Model from Rough Approximation of Preference Relation
Oxford, England
August 26-August 29
ISBN: 0-7695-1727-7
ASCII Text
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Roman Slowinski, Salvatore Greco, Benedetto Matarazzo, "Mining Decision-Rule Preference Model from Rough Approximation of Preference Relation," Computer Software and Applications Conference, Annual International, pp. 1129, 26th Annual International Computer Software and Applications Conference, 2002.
 
 
BibTex
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@article{ 10.1109/CMPSAC.2002.1045163,
author = {Roman Slowinski and Salvatore Greco and Benedetto Matarazzo},
title = {Mining Decision-Rule Preference Model from Rough Approximation of Preference Relation},
journal ={Computer Software and Applications Conference, Annual International},
volume = {0},
year = {2002},
issn = {0730-3157},
pages = {1129},
doi = {http://doi.ieeecomputersociety.org/10.1109/CMPSAC.2002.1045163},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Software and Applications Conference, Annual International
TI - Mining Decision-Rule Preference Model from Rough Approximation of Preference Relation
SN - 0730-3157
SP
EP
A1 - Roman Slowinski,
A1 - Salvatore Greco,
A1 - Benedetto Matarazzo,
PY - 2002
KW - null
VL - 0
JA - Computer Software and Applications Conference, Annual International
ER -
 
Roman Slowinski, Poznan University of Technology
Salvatore Greco, University of Catania
Benedetto Matarazzo, University of Catania
Given a ranking of actions evaluated by a set of evaluation criteria, we are constructing a rough approximation of the preference relation known from this ranking. The rough approximation of the preference relation is a starting point for mining "if…, then…" decision rules constituting a symbolic preference model. The set of rules is induced such as to be compatible with a concordance-discordance preference model used in well-known multicriteria decision aiding methods. Application of the set of decision rules to a new set of actions gives a fuzzy outranking graph. Positive and negative flows are calculated for each action in the graph, giving arguments about its strength and weakness. Aggregation of both arguments leads to a final ranking, either partial or complete. The approach can be applied to support multicriteria choice and ranking of actions when the input information is a ranking of some reference actions.
Citation:
Roman Slowinski, Salvatore Greco, Benedetto Matarazzo, "Mining Decision-Rule Preference Model from Rough Approximation of Preference Relation," compsac, pp.1129, 26th Annual International Computer Software and Applications Conference, 2002
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