A probabilistic multiple criteria sorting approach based on distance functions

B. Celik Aydin, E. Karasakal, C. Iyigün

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods.

LanguageEnglish
Pages3610-3618
Number of pages9
JournalExpert Systems with Applications
Volume42
Issue number7
DOIs
StatePublished - 1 May 2015

Fingerprint

Sorting
Experiments

Keywords

  • Distance function based sorting
  • Multiple criteria sorting
  • Probabilistic sorting

Cite this

@article{00dfdad79280493b9cc2eaaa94dd0cec,
title = "A probabilistic multiple criteria sorting approach based on distance functions",
abstract = "In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods.",
keywords = "Distance function based sorting, Multiple criteria sorting, Probabilistic sorting",
author = "{Celik Aydin}, B. and E. Karasakal and C. Iyig{\"u}n",
year = "2015",
month = "5",
day = "1",
doi = "10.1016/j.eswa.2014.11.049",
language = "English",
volume = "42",
pages = "3610--3618",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier",
number = "7",

}

A probabilistic multiple criteria sorting approach based on distance functions. / Celik Aydin, B.; Karasakal, E.; Iyigün, C.

In: Expert Systems with Applications, Vol. 42, No. 7, 01.05.2015, p. 3610-3618.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A probabilistic multiple criteria sorting approach based on distance functions

AU - Celik Aydin,B.

AU - Karasakal,E.

AU - Iyigün,C.

PY - 2015/5/1

Y1 - 2015/5/1

N2 - In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods.

AB - In this paper, a new probabilistic distance based sorting (PDIS) method is developed for multiple criteria sorting problems. The distance to the ideal point is used as a criteria disaggregation function to determine the values of alternatives. These values are used to sort alternatives into the predefined classes. The method also calculates probabilities that each alternative belong to the predefined classes in order to handle alternative optimal solutions. It is applied to five data sets and its performance is compared with two well-known methods from literature. Computational experiments show that the PDIS method performs better than the other methods.

KW - Distance function based sorting

KW - Multiple criteria sorting

KW - Probabilistic sorting

UR - http://www.scopus.com/inward/record.url?scp=84920973941&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2014.11.049

DO - 10.1016/j.eswa.2014.11.049

M3 - Article

VL - 42

SP - 3610

EP - 3618

JO - Expert Systems with Applications

T2 - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 7

ER -