A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I

The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 18; H. 1; S. 4 - 19
Hauptverfasser: Mukhopadhyay, Anirban, Maulik, Ujjwal, Bandyopadhyay, Sanghamitra, Coello Coello, Carlos Artemio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Association rules
Biological cells
Classification
Construction
Data mining
Data models
Evolutionary
Evolutionary algorithms
Evolutionary computation
feature selection
Genetic algorithms
Itemsets
Mathematical models
multiobjective evolutionary algorithms
Optimization
Pareto optimality
Tasks
ISSN:1089-778X, 1941-0026
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Zusammenfassung:The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjective evolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2013.2290086