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Data mining methods for knowledge discovery in multi-objective optimization: Part B - New developments and applications
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-5436-2128
University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
Department of Electrical and Computer Engineering, Michigan State University, USA.ORCID iD: 0000-0001-7402-9939
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, 119-138 p.Article in journal (Refereed) Published
Abstract [en]

The first part of this paper served as a comprehensive survey of data mining methods that have been used to extract knowledge from solutions generated during multi-objective optimization. The current paper addresses three major shortcomings of existing methods, namely, lack of interactiveness in the objective space, inability to handle discrete variables and inability to generate explicit knowledge. Four data mining methods are developed that can discover knowledge in the decision space and visualize it in the objective space. These methods are (i) sequential pattern mining, (ii) clustering-based classification trees, (iii) hybrid learning, and (iv) flexible pattern mining. Each method uses a unique learning strategy to generate explicit knowledge in the form of patterns, decision rules and unsupervised rules. The methods are also capable of taking the decision maker's preferences into account to generate knowledge unique to preferred regions of the objective space. Three realistic production systems involving different types of discrete variables are chosen as application studies. A multi-objective optimization problem is formulated for each system and solved using NSGA-II to generate the optimization datasets. Next, all four methods are applied to each dataset. In each application, the methods discover similar knowledge for specified regions of the objective space. Overall, the unsupervised rules generated by flexible pattern mining are found to be the most consistent, whereas the supervised rules from classification trees are the most sensitive to user-preferences. 

Place, publisher, year, edition, pages
2017. Vol. 70, 119-138 p.
Keyword [en]
Data mining, Knowledge discovery, Multi-objective optimization, Discrete variables, Production systems, Flexible pattern mining
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13266DOI: 10.1016/j.eswa.2016.10.016ISI: 000389162000008ScopusID: 2-s2.0-84995977095OAI: oai:DiVA.org:his-13266DiVA: diva2:1060705
Projects
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Funder
Knowledge Foundation, 41231
Available from: 2016-12-29 Created: 2016-12-29 Last updated: 2017-01-20Bibliographically approved

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The full text will be freely available from 2019-01-01 01:00
Available from 2019-01-01 01:00

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Bandaru, SunithNg, Amos H. C.Deb, Kalyanmoy
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