5 Search Results for "Slowinski, Roman"

Document
DOI: 10.4230/DagRep.4.3.1
Preference Learning (Dagstuhl Seminar 14101)

Authors: Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner

Published in: Dagstuhl Reports, Volume 4, Issue 3 (2014)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14101 "Preference Learning". Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The goal of this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies.
Cite as

Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner. Preference Learning (Dagstuhl Seminar 14101). In Dagstuhl Reports, Volume 4, Issue 3, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{furnkranz_et_al:DagRep.4.3.1,
  author =	{F\"{u}rnkranz, Johannes and H\"{u}llermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott},
  title =	{{Preference Learning (Dagstuhl Seminar 14101)}},
  pages =	{1--27},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{3},
  editor =	{F\"{u}rnkranz, Johannes and H\"{u}llermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.4.3.1},
  URN =		{urn:nbn:de:0030-drops-45506},
  doi =		{10.4230/DagRep.4.3.1},
  annote =	{Keywords: machine learning, preference learning, preference elicitation, ranking, social choice, multiple criteria decision making, decision under risk and unce information retrieval}
}
Document
DOI: 10.4230/DagSemProc.09041.6
09041 Working Group on MCDM for Robust Multiobjective Optimization (1st Round)

Authors: Jos Figueira, Martin Geiger, Salvatore Greco, Johannes Jahn, Kathrin Klamroth, Masahiro Inuiguchi, Vincent Mousseau, Sayin Serpil, Roman Slowinski, Margaret M. Wiecek, and Witting Katrin

Published in: Dagstuhl Seminar Proceedings, Volume 9041, Hybrid and Robust Approaches to Multiobjective Optimization (2009)

Abstract
This group explored MCDM techniques for robust multiobjective optimization.
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Jos Figueira, Martin Geiger, Salvatore Greco, Johannes Jahn, Kathrin Klamroth, Masahiro Inuiguchi, Vincent Mousseau, Sayin Serpil, Roman Slowinski, Margaret M. Wiecek, and Witting Katrin. 09041 Working Group on MCDM for Robust Multiobjective Optimization (1st Round). In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{figueira_et_al:DagSemProc.09041.6,
  author =	{Figueira, Jos and Geiger, Martin and Greco, Salvatore and Jahn, Johannes and Klamroth, Kathrin and Inuiguchi, Masahiro and Mousseau, Vincent and Sayin Serpil and Slowinski, Roman and Wiecek, Margaret M. and Witting Katrin},
  title =	{{09041 Working Group on MCDM for Robust Multiobjective Optimization (1st Round)}},
  booktitle =	{Hybrid and Robust Approaches to Multiobjective Optimization},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9041},
  editor =	{Kalyanmoy Deb and Salvatore Greco and Kaisa Miettinen and Eckart Zitzler},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.09041.6},
  URN =		{urn:nbn:de:0030-drops-20025},
  doi =		{10.4230/DagSemProc.09041.6},
  annote =	{Keywords: Robust multiobjective optimization}
}
Document
DOI: 10.4230/DagSemProc.06501.1
06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization

Authors: Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski

Published in: Dagstuhl Seminar Proceedings, Volume 6501, Practical Approaches to Multi-Objective Optimization (2007)

Abstract
From 10.12.06 to 15.12.06, the Dagstuhl Seminar 06501 ``Practical Approaches to Multi-Objective Optimization'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Jürgen Branke, Kalyanmoy Deb, Kaisa Miettinen, and Roman Slowinski. 06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 6501, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{branke_et_al:DagSemProc.06501.1,
  author =	{Branke, J\"{u}rgen and Deb, Kalyanmoy and Miettinen, Kaisa and Slowinski, Roman},
  title =	{{06501 Abstracts Collection – Practical Approaches to Multi-Objective Optimization}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6501},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman Slowinski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06501.1},
  URN =		{urn:nbn:de:0030-drops-11224},
  doi =		{10.4230/DagSemProc.06501.1},
  annote =	{Keywords: Multi-criteria optimization, evolutionary and classical methods, interaction}
}
Document
DOI: 10.4230/DagSemProc.06501.2
Reference Point Approaches and Objective Ranking

Authors: Andrzej P. Wierzbicki

Published in: Dagstuhl Seminar Proceedings, Volume 6501, Practical Approaches to Multi-Objective Optimization (2007)

Abstract
The paper presents a reflection on some of the basic assumptions and philosophy of reference point approaches, stressing their unique concentra-tion on the sovereignty of the subjective decision maker. As a new devel-opment in reference point approaches also the concept of objective ranking is stressed, defined as dependent only on a given set of data, relevant for the decision situation, and independent from any more detailed specifica-tion of personal preferences than that given by defining criteria and the partial order in criterion space. Rational objective ranking can be based on reference point approach, because reference levels needed in this approach can be established objectively statistically from the given data set. Exam-ples show that such objective ranking can be very useful in many man-agement situations.
Cite as

Andrzej P. Wierzbicki. Reference Point Approaches and Objective Ranking. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 6501, pp. 1-20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)

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@InProceedings{wierzbicki:DagSemProc.06501.2,
  author =	{Wierzbicki, Andrzej P.},
  title =	{{Reference Point Approaches and Objective Ranking}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--20},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{6501},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Roman Slowinski},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06501.2},
  URN =		{urn:nbn:de:0030-drops-11210},
  doi =		{10.4230/DagSemProc.06501.2},
  annote =	{Keywords: Multiple criteria optimization and decisions; reference point approaches; objectivity and subjectivity in decision support}
}
Document
DOI: 10.4230/DagSemProc.04461.12
Multi-criteria ranking of a finite set of alternatives using ordinal regression and additive utility functions - a new UTA-GMS method

Authors: Roman Slowinski, Salvatore Greco, and Vincent Mousseau

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
UTA-GMS is a new method for assessment of strong or weak outranking relation in a problem of multi-criteria ranking, proposed by the authors. The ranking concerns a finite but relatively large set of alternatives A. We assume indirect preference information supplied by the decision maker (DM) in form of a complete preorder on a subset of reference alternatives R, called reference preorder. The preference model build from this information is an additive value function. The technique of passing from reference preorder to compatible additive value functions is called ordinal regression and it is well known from the UTA method proposed by Jacquet-Lagreze and Siskos in 1982. Unlike in the UTA method, we take into account all compatible value functions (instead of one or several most characteristic) at the stage of ranking the whole set A of alternatives. Moreover, we do not impose the additive value function to have piecewise-linear components but we accept any additive form. The resulting relations in A are twofold: strong outranking (if alternative x has greater value than y for all compatible value functions) and weak outranking (if alternative x has greater value than y for at least one compatible value function). Strong outranking is a partial preorder and weak outranking is a complete preorder in A. The strong outranking is of particular interest for the DM – it corresponds to dominance relation when the set of reference alternatives is empty, and to a complete preorder relation when the reference ranking is compatible with a single value function only. This approach has several interesting extensions useful for practical applications. The method has been implemented for a PC and will be presented together with an example of application.
Cite as

Roman Slowinski, Salvatore Greco, and Vincent Mousseau. Multi-criteria ranking of a finite set of alternatives using ordinal regression and additive utility functions - a new UTA-GMS method. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{slowinski_et_al:DagSemProc.04461.12,
  author =	{Slowinski, Roman and Greco, Salvatore and Mousseau, Vincent},
  title =	{{Multi-criteria ranking of a finite set of alternatives using ordinal regression and additive utility functions - a new UTA-GMS method}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.12},
  URN =		{urn:nbn:de:0030-drops-2476},
  doi =		{10.4230/DagSemProc.04461.12},
  annote =	{Keywords: Multiple-criteria ranking, ordinal regression, partial preorder, UTA-like method}
}
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