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Decision rule approach

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Decision rule approach. / Greco, Salvatore; Matarazzo, Benedetto; Słowiński, Roman.

Multiple criteria decision analysis: State of the art surveys. ed. / Salvatore Greco; Matthias Ehrgott; José Rui Figueira. New York : Springer New York, 2016. p. 497-552 (International Series in Operations Research & Management Science; Vol. 233).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Harvard

Greco, S, Matarazzo, B & Słowiński, R 2016, Decision rule approach. in S Greco, M Ehrgott & JR Figueira (eds), Multiple criteria decision analysis: State of the art surveys. International Series in Operations Research & Management Science, vol. 233, Springer New York, New York, pp. 497-552. https://doi.org/10.1007/978-1-4939-3094-4_13

APA

Greco, S., Matarazzo, B., & Słowiński, R. (2016). Decision rule approach. In S. Greco, M. Ehrgott, & J. R. Figueira (Eds.), Multiple criteria decision analysis: State of the art surveys (pp. 497-552). (International Series in Operations Research & Management Science; Vol. 233). Springer New York. https://doi.org/10.1007/978-1-4939-3094-4_13

Vancouver

Greco S, Matarazzo B, Słowiński R. Decision rule approach. In Greco S, Ehrgott M, Figueira JR, editors, Multiple criteria decision analysis: State of the art surveys. New York: Springer New York. 2016. p. 497-552. (International Series in Operations Research & Management Science). https://doi.org/10.1007/978-1-4939-3094-4_13

Author

Greco, Salvatore ; Matarazzo, Benedetto ; Słowiński, Roman. / Decision rule approach. Multiple criteria decision analysis: State of the art surveys. editor / Salvatore Greco ; Matthias Ehrgott ; José Rui Figueira. New York : Springer New York, 2016. pp. 497-552 (International Series in Operations Research & Management Science).

Bibtex

@inbook{4d44d8f1f2044ef5b72ab061a75c0e62,
title = "Decision rule approach",
abstract = "In this chapter we present the methodology of Multiple-Criteria Decision Aiding (MCDA) based on preference modelling in terms of “if…, then …” decision rules. The basic assumption of the decision rule approach is that the decision maker (DM) accepts to give preference information in terms of examples of decisions and looks for simple rules justifying her decisions. An important advantage of this approach is the possibility of handling inconsistencies in the preference information, resulting from hesitations of the DM. The proposed methodology is based on the elementary, natural and rational principle of dominance. It says that if action x is at least as good as action y on each criterion from a considered family, then x is also comprehensively at least as good as y. The set of decision rules constituting the preference model is induced from the preference information using a knowledge discovery technique properly adapted in order to handle the dominance principle. The mathematical basis of the decision rule approach to MCDA is the Dominance-based Rough Set Approach (DRSA) developed by the authors. We present some basic applications of this approach, starting from multiple-criteria classification problems, and then going through decision under uncertainty, hierarchical decision making, classification problems with partially missing information, problems with imprecise information modelled by fuzzy sets, until multiple-criteria choice and ranking problems, and some classical problems of operations research. All these applications are illustrated by didactic examples whose aim is to show in an easy way how DRSA can be used in various contexts of MCDA.",
keywords = "Rough set, Dominance based Rough Set Approach, Rough approximations, Decision rule",
author = "Salvatore Greco and Benedetto Matarazzo and Roman S{\l}owi{\'n}ski",
year = "2016",
month = mar,
doi = "10.1007/978-1-4939-3094-4_13",
language = "English",
isbn = "978-1-4939-3093-7",
series = "International Series in Operations Research & Management Science",
publisher = "Springer New York",
pages = "497--552",
editor = "Salvatore Greco and Matthias Ehrgott and Figueira, {Jos{\'e} Rui}",
booktitle = "Multiple criteria decision analysis",
address = "United States",

}

RIS

TY - CHAP

T1 - Decision rule approach

AU - Greco, Salvatore

AU - Matarazzo, Benedetto

AU - Słowiński, Roman

PY - 2016/3

Y1 - 2016/3

N2 - In this chapter we present the methodology of Multiple-Criteria Decision Aiding (MCDA) based on preference modelling in terms of “if…, then …” decision rules. The basic assumption of the decision rule approach is that the decision maker (DM) accepts to give preference information in terms of examples of decisions and looks for simple rules justifying her decisions. An important advantage of this approach is the possibility of handling inconsistencies in the preference information, resulting from hesitations of the DM. The proposed methodology is based on the elementary, natural and rational principle of dominance. It says that if action x is at least as good as action y on each criterion from a considered family, then x is also comprehensively at least as good as y. The set of decision rules constituting the preference model is induced from the preference information using a knowledge discovery technique properly adapted in order to handle the dominance principle. The mathematical basis of the decision rule approach to MCDA is the Dominance-based Rough Set Approach (DRSA) developed by the authors. We present some basic applications of this approach, starting from multiple-criteria classification problems, and then going through decision under uncertainty, hierarchical decision making, classification problems with partially missing information, problems with imprecise information modelled by fuzzy sets, until multiple-criteria choice and ranking problems, and some classical problems of operations research. All these applications are illustrated by didactic examples whose aim is to show in an easy way how DRSA can be used in various contexts of MCDA.

AB - In this chapter we present the methodology of Multiple-Criteria Decision Aiding (MCDA) based on preference modelling in terms of “if…, then …” decision rules. The basic assumption of the decision rule approach is that the decision maker (DM) accepts to give preference information in terms of examples of decisions and looks for simple rules justifying her decisions. An important advantage of this approach is the possibility of handling inconsistencies in the preference information, resulting from hesitations of the DM. The proposed methodology is based on the elementary, natural and rational principle of dominance. It says that if action x is at least as good as action y on each criterion from a considered family, then x is also comprehensively at least as good as y. The set of decision rules constituting the preference model is induced from the preference information using a knowledge discovery technique properly adapted in order to handle the dominance principle. The mathematical basis of the decision rule approach to MCDA is the Dominance-based Rough Set Approach (DRSA) developed by the authors. We present some basic applications of this approach, starting from multiple-criteria classification problems, and then going through decision under uncertainty, hierarchical decision making, classification problems with partially missing information, problems with imprecise information modelled by fuzzy sets, until multiple-criteria choice and ranking problems, and some classical problems of operations research. All these applications are illustrated by didactic examples whose aim is to show in an easy way how DRSA can be used in various contexts of MCDA.

KW - Rough set

KW - Dominance based Rough Set Approach

KW - Rough approximations

KW - Decision rule

U2 - 10.1007/978-1-4939-3094-4_13

DO - 10.1007/978-1-4939-3094-4_13

M3 - Chapter (peer-reviewed)

SN - 978-1-4939-3093-7

T3 - International Series in Operations Research & Management Science

SP - 497

EP - 552

BT - Multiple criteria decision analysis

A2 - Greco, Salvatore

A2 - Ehrgott, Matthias

A2 - Figueira, José Rui

PB - Springer New York

CY - New York

ER -

ID: 4293388