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Data mining methods for knowledge discovery in multi-objective optimization : Part A - Survey

Bandaru, Sunith (författare)
Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
Ng, Amos H. C. (författare)
Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
Deb, Kalyanmoy (författare)
Department of Electrical and Computer Engineering, Michigan State University, USA
 (creator_code:org_t)
Elsevier, 2017
2017
Engelska.
Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 70, s. 139-159
  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
Stäng  
  • Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. 

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Data mining
Multi-objective optimization
Descriptive statistics
Visual data mining
Machine learning
Knowledge-driven optimization
Technology
Teknik
Production and Automation Engineering
Produktion och automatiseringsteknik
INF201 Virtual Production Development
INF201 Virtual Production Development
VF-KDO
VF-KDO

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Av författaren/redakt...
Bandaru, Sunith
Ng, Amos H. C.
Deb, Kalyanmoy
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NATURVETENSKAP
och Data och informa ...
och Datavetenskap
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Expert systems w ...
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Högskolan i Skövde

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