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. Nga ; amos.ng@his.se" class="auth_mail" title="E-mail the corresponding author ; Kalyanmoy Debb ; 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
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Data mining methods for extracting knowledge from multi-objective optimization are reviewed.
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Methods are classified based on the type and form of knowledge generated.
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Descriptive statistics, visual data mining and machine learning methods are discussed.
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Limitations of existing methods are discussed.
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A generic framework for knowledge-driven optimization is proposed.