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

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

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作者：Sunith Bandaru ; ^a ; ^{sunith.bandaru@his.se" class="auth_mail" title="E-mail the corresponding author} ; ^{sunith.bandaru@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Amos H.C. Ng^a ; ^{amos.ng@his.se" class="auth_mail" title="E-mail the corresponding author} ; Kalyanmoy Deb^b ; ^{kdeb@egr.msu.edu" class="auth_mail" title="E-mail the corresponding author}
关键词：Data mining ; Multi-objective optimization ; Descriptive statistics ; Visual data mining ; Machine learning ; Knowledge-driven optimization
刊名：Expert Systems with Applications
出版年：2017
出版时间：15 March 2017
年：2017
卷：70
期：Complete
页码：139-159
全文大小：3443 K

文摘

•: Data mining methods for extracting knowledge from multi-objective optimization are reviewed.
•: Methods are classified based on the type and form of knowledge generated.
•: Descriptive statistics, visual data mining and machine learning methods are discussed.
•: Limitations of existing methods are discussed.
•: A generic framework for knowledge-driven optimization is proposed.

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