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NAME

r.mcda.roughset - Generates a MCDA map from several criteria maps using Dominance Rough Set Approach.

KEYWORDS

raster, Dominance Rough Set Approach, Multi Criteria Decision Analysis (MCDA)

SYNOPSIS

r.mcda.roughset
r.mcda.roughset --help
r.mcda.roughset [-ln] criteria=name[,name,...] preferences=character decision=name outputMap=string outputTxt=name [--help] [--verbose] [--quiet] [--ui]

Flags:

-l
do not remove single rules in vector format
-n
compute null value as zero
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog

Parameters:

criteria=name[,name,...] [required]
Name of criteria raster maps
preferences=character [required]
gain,cost
decision=name [required]
Name of decision raster map
outputMap=string [required]
Output classified raster map
outputTxt=name [required]
Name for output files (base for *.isf and *.rls files)
Default: infosys

Table of contents

DESCRIPTION

r.mcda.roughset is the python implementation of the dominance rough set approach (Domlem algorithm) in GRASS GIS environment. It requires the following input:
1. the geographical criteria constituting the information system for the rough set analysis; they have to describe environmental, economic or social issues(criteria=name[,name,...]);
2. the preference (preferences=character)for each criteria used in analysis (gain or cost with comma separator)
3. the theme in which areas with the issues to be studied are classified (with crescent preference values) (decision=string).

An information system is generated and Domlem algorithm is applied for extraction a minimal set of rules.

The algorithm builds two text files (outputTxt=name): the first with isf extension for more deep analysis with non geographic software like 4emka and JAMM ; the second file with rls extension hold all the set of rules generate. An output map (outputMap=string)is generated for region classification with the rules finded and the criteria stored in GRASS geodb.

NOTES

The module can work very slowly with high number of criteria and sample. For bug please contact Gianluca Massei (g_mass@libero.it)

REFERENCE

  1. Greco S., Matarazzo B., Slowinski R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129, 1 (2001) 1-47.

  2. Greco S., Matarazzo B., Slowinski R.: Multicriteria classification by dominance-based rough set approach. In: W.Kloesgen and J.Zytkow (eds.), Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York, 2002.

  3. Greco S., Matarazzo B., Slowinski, R., Stefanowski, J.: An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle. In W. Ziarko, Y. Yao (eds.): Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence 2005 (2001) 304 - 313. Springer-Verlag

  4. Greco, S., B. Matarazzo, R. Slowinski and J. Stefanowski: Variable consistency model of dominance-based rough set approach. In W.Ziarko, Y.Yao (eds.): Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence 2005 (2001) 170 - 181. Springer-Verlag

  5. http://en.wikipedia.org/wiki/Dominance-based_rough_set_approach - “Dominance-based rough set approach”

  6. http://idss.cs.put.poznan.pl/site/software.html - Software from Laboratory of intelligent decision support system in Poznam University of Technology

SEE ALSO

r.mcda.fuzzy, r.mcda.electre, r.mcda.regime, r.to.drsa, r.in.drsa

AUTHORS

Antonio Boggia - Gianluca Massei
Department of Economics and Appraisal - University of Perugia - Italy

Last changed: $Date: 2014-01-21 23:14:02 +0100 (Tue, 21 Jan 2014) $

SOURCE CODE

Available at: r.mcda.roughset source code (history)


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