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Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Simulation-Based Optimization)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Simulation-Based Optimization)ORCID-id: 0000-0003-0111-1776
2019 (engelsk)Inngår i: Evolutionary Multi-Criterion Optimization: 10th International Conference, EMO 2019, East Lansing, MI, USA, March 10-13, 2019, Proceedings / [ed] Kalyanmoy Deb, Erik Goodman, Carlos A. Coello Coello, Kathrin Klamroth, Kaisa Miettinen, Sanaz Mostaghim, Patrick Reed, Cham, Switzerland: Springer, 2019, Vol. 11411, s. 605-617Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.

sted, utgiver, år, opplag, sider
Cham, Switzerland: Springer, 2019. Vol. 11411, s. 605-617
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11411
Emneord [en]
Visualization, Data mining, Multi-criteria decision making, Decision space, Trend analysis, Objective space
HSV kategori
Forskningsprogram
Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-16712DOI: 10.1007/978-3-030-12598-1_48Scopus ID: 2-s2.0-85063032277ISBN: 978-3-030-12597-4 (tryckt)ISBN: 978-3-030-12598-1 (digital)OAI: oai:DiVA.org:his-16712DiVA, id: diva2:1298632
Konferanse
10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, MI, USA, March 10-13, 2019
Prosjekter
Knowledge-Driven Decision Support (KDDS)
Forskningsfinansiär
Knowledge Foundation, 41231
Merknad

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 11411)

Tilgjengelig fra: 2019-03-25 Laget: 2019-03-25 Sist oppdatert: 2019-05-23bibliografisk kontrollert

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Fulltekst tilgjengelig fra 2020-02-03 00:00
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