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Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions

Publikation: Forskning - fagfællebedømtTidsskriftartikel

Standard

Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions. / Franco de los Ríos, Camilo; Hougaard, Jens Leth; Nielsen, Kurt.

I: Applied Soft Computing, 22.07.2017.

Publikation: Forskning - fagfællebedømtTidsskriftartikel

Harvard

Franco de los Ríos, C, Hougaard, JL & Nielsen, K 2017, 'Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions' Applied Soft Computing. DOI: 10.1016/j.asoc.2017.07.031

APA

Franco de los Ríos, C., Hougaard, J. L., & Nielsen, K. (2017). Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions. Applied Soft Computing. DOI: 10.1016/j.asoc.2017.07.031

Vancouver

Franco de los Ríos C, Hougaard JL, Nielsen K. Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions. Applied Soft Computing. 2017 jul 22. Tilgængelig fra, DOI: 10.1016/j.asoc.2017.07.031

Author

Franco de los Ríos, Camilo; Hougaard, Jens Leth; Nielsen, Kurt / Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions.

I: Applied Soft Computing, 22.07.2017.

Publikation: Forskning - fagfællebedømtTidsskriftartikel

Bibtex

@article{2be6f3b9fd5e4117b904d5ba656868b3,
title = "Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions",
abstract = "This paper proposes an interval-valued multi-criteria method for learning preferences and attitudes, identifying priorities with maximal robustness for decision support. The method is based on the notion of weighted overlap dominance, formalized by means of aggregation operators and interval-valued fuzzy sets. The procedure handles uncertainty by estimating the likelihood of dominance among pairs of alternatives, inducing an attitude-based system of dominance and indifference relations. This system allows conflicting situations of indifference/dependency to arise, which need to be resolved for properly identifying preferences under any attitude. In order to do so, relevance functions are examined over the whole system of relations, obtaining a weak preference order together with its associated attitude and robustness index. As a result, the proposed method allows learning preferences and attitudes, identifying the solutions with maximal robustness for intelligent decision support.",
author = "{Franco de los Ríos}, Camilo and Hougaard, {Jens Leth} and Kurt Nielsen",
year = "2017",
month = "7",
doi = "10.1016/j.asoc.2017.07.031",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Learning preferences and attitudes by multi-criteria overlap dominance and relevance functions

AU - Franco de los Ríos,Camilo

AU - Hougaard,Jens Leth

AU - Nielsen,Kurt

PY - 2017/7/22

Y1 - 2017/7/22

N2 - This paper proposes an interval-valued multi-criteria method for learning preferences and attitudes, identifying priorities with maximal robustness for decision support. The method is based on the notion of weighted overlap dominance, formalized by means of aggregation operators and interval-valued fuzzy sets. The procedure handles uncertainty by estimating the likelihood of dominance among pairs of alternatives, inducing an attitude-based system of dominance and indifference relations. This system allows conflicting situations of indifference/dependency to arise, which need to be resolved for properly identifying preferences under any attitude. In order to do so, relevance functions are examined over the whole system of relations, obtaining a weak preference order together with its associated attitude and robustness index. As a result, the proposed method allows learning preferences and attitudes, identifying the solutions with maximal robustness for intelligent decision support.

AB - This paper proposes an interval-valued multi-criteria method for learning preferences and attitudes, identifying priorities with maximal robustness for decision support. The method is based on the notion of weighted overlap dominance, formalized by means of aggregation operators and interval-valued fuzzy sets. The procedure handles uncertainty by estimating the likelihood of dominance among pairs of alternatives, inducing an attitude-based system of dominance and indifference relations. This system allows conflicting situations of indifference/dependency to arise, which need to be resolved for properly identifying preferences under any attitude. In order to do so, relevance functions are examined over the whole system of relations, obtaining a weak preference order together with its associated attitude and robustness index. As a result, the proposed method allows learning preferences and attitudes, identifying the solutions with maximal robustness for intelligent decision support.

U2 - 10.1016/j.asoc.2017.07.031

DO - 10.1016/j.asoc.2017.07.031

M3 - Journal article

JO - Applied Soft Computing

T2 - Applied Soft Computing

JF - Applied Soft Computing

SN - 1568-4946

ER -

ID: 182090648