A method for ordinal classification in multicriteria decision making

Risto Lahdelma; Pekka Salminen

A method for ordinal classification in multicriteria decision making

Risto Lahdelma^*
, Pekka Salminen

^*Corresponding author for this work

University of Jyväskylä

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

11 Citations (Scopus)

Abstract

In a discrete multicriteria decision problem, a finite set of alternatives are evaluated in terms of multiple criteria. When the goal is to partition the alternatives into predefined ordered categories, this is called an ordinal classification or sorting problem. In this paper we present the new SMAA-OC method for the ordinal classification problem that can handle uncertain, imprecise or partially missing criteria and preference information. SMAA-OC is based on a utility or value function to represent the DMs' preference structure and boundary profiles to define the categories. The method is implemented through stochastic simulation and statistical analysis. We demonstrate the method with a small example.

Original language	English
Title of host publication	Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010
Pages	420-425
Number of pages	6
Publication status	Published - 2010
MoE publication type	A4 Conference publication
Event	IASTED International Conference on Artificial Intelligence and Applications - Innsbruck, Austria Duration: 15 Feb 2010 → 17 Feb 2010 Conference number: 10

Conference

Conference	IASTED International Conference on Artificial Intelligence and Applications
Abbreviated title	AIA
Country/Territory	Austria
City	Innsbruck
Period	15/02/2010 → 17/02/2010

Keywords

Classification
Decision support
Multicriteria analysis
Probabilistic Reasoning

Cite this

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title = "A method for ordinal classification in multicriteria decision making",

abstract = "In a discrete multicriteria decision problem, a finite set of alternatives are evaluated in terms of multiple criteria. When the goal is to partition the alternatives into predefined ordered categories, this is called an ordinal classification or sorting problem. In this paper we present the new SMAA-OC method for the ordinal classification problem that can handle uncertain, imprecise or partially missing criteria and preference information. SMAA-OC is based on a utility or value function to represent the DMs' preference structure and boundary profiles to define the categories. The method is implemented through stochastic simulation and statistical analysis. We demonstrate the method with a small example.",

keywords = "Classification, Decision support, Multicriteria analysis, Probabilistic Reasoning",

author = "Risto Lahdelma and Pekka Salminen",

year = "2010",

language = "English",

isbn = "9780889868182",

pages = "420--425",

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note = "IASTED International Conference on Artificial Intelligence and Applications, AIA ; Conference date: 15-02-2010 Through 17-02-2010",

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T1 - A method for ordinal classification in multicriteria decision making

AU - Lahdelma, Risto

AU - Salminen, Pekka

N1 - Conference code: 10

PY - 2010

Y1 - 2010

N2 - In a discrete multicriteria decision problem, a finite set of alternatives are evaluated in terms of multiple criteria. When the goal is to partition the alternatives into predefined ordered categories, this is called an ordinal classification or sorting problem. In this paper we present the new SMAA-OC method for the ordinal classification problem that can handle uncertain, imprecise or partially missing criteria and preference information. SMAA-OC is based on a utility or value function to represent the DMs' preference structure and boundary profiles to define the categories. The method is implemented through stochastic simulation and statistical analysis. We demonstrate the method with a small example.

AB - In a discrete multicriteria decision problem, a finite set of alternatives are evaluated in terms of multiple criteria. When the goal is to partition the alternatives into predefined ordered categories, this is called an ordinal classification or sorting problem. In this paper we present the new SMAA-OC method for the ordinal classification problem that can handle uncertain, imprecise or partially missing criteria and preference information. SMAA-OC is based on a utility or value function to represent the DMs' preference structure and boundary profiles to define the categories. The method is implemented through stochastic simulation and statistical analysis. We demonstrate the method with a small example.

KW - Classification

KW - Decision support

KW - Multicriteria analysis

KW - Probabilistic Reasoning

UR - https://www.scopus.com/pages/publications/77954599243

M3 - Conference article in proceedings

AN - SCOPUS:77954599243

SN - 9780889868182

SP - 420

EP - 425

BT - Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010

T2 - IASTED International Conference on Artificial Intelligence and Applications

Y2 - 15 February 2010 through 17 February 2010

ER -

A method for ordinal classification in multicriteria decision making

Abstract

Conference

Keywords

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