Fuzzy classification with multi-objective evolutionary algorithms

Fernando Jiménez; Gracia Sánchez; José F. Sánchez; José M. Alcaraz

doi:10.1007/978-3-540-87656-4_90

Fuzzy classification with multi-objective evolutionary algorithms

Fernando Jiménez^*, Gracia Sánchez, José F. Sánchez, José M. Alcaraz

^*Corresponding author for this work

Research Centre for Cyber Security, Privacy and Trust

Facultad de Informática

Research output: Chapter in Book/Published conference output › Conference publication

3 Citations (Scopus)

Abstract

In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.

Original language	English
Title of host publication	Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings
Pages	730-738
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-540-87656-4_90
Publication status	Published - 2008
Event	3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008 - Burgos, Spain Duration: 24 Sept 2008 → 26 Sept 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5271 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008
Country/Territory	Spain
City	Burgos
Period	24/09/08 → 26/09/08

Keywords

Fuzzy classification
Multi-objective evolutionary algorithms

Access to Document

10.1007/978-3-540-87656-4_90

Cite this

Jiménez, F., Sánchez, G., Sánchez, J. F., & Alcaraz, J. M. (2008). Fuzzy classification with multi-objective evolutionary algorithms. In Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings (pp. 730-738). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5271 LNAI). https://doi.org/10.1007/978-3-540-87656-4_90

Jiménez, Fernando ; Sánchez, Gracia ; Sánchez, José F. et al. / Fuzzy classification with multi-objective evolutionary algorithms. Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings. 2008. pp. 730-738 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Fuzzy classification with multi-objective evolutionary algorithms",

abstract = "In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.",

keywords = "Fuzzy classification, Multi-objective evolutionary algorithms",

author = "Fernando Jim{\'e}nez and Gracia S{\'a}nchez and S{\'a}nchez, {Jos{\'e} F.} and Alcaraz, {Jos{\'e} M.}",

year = "2008",

doi = "10.1007/978-3-540-87656-4_90",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008 ; Conference date: 24-09-2008 Through 26-09-2008",

}

Jiménez, F, Sánchez, G, Sánchez, JF & Alcaraz, JM 2008, Fuzzy classification with multi-objective evolutionary algorithms. in Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5271 LNAI, pp. 730-738, 3rd International Workshop on Hybrid Artificial Intelligence Systems, HAIS 2008, Burgos, Spain, 24/09/08. https://doi.org/10.1007/978-3-540-87656-4_90

Fuzzy classification with multi-objective evolutionary algorithms. / Jiménez, Fernando; Sánchez, Gracia; Sánchez, José F. et al.
Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings. 2008. p. 730-738 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5271 LNAI).

Research output: Chapter in Book/Published conference output › Conference publication

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AU - Alcaraz, José M.

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AB - In this work we propose, on the one hand, a multi-objective constrained optimization model to obtain fuzzy models for classification considering criteria of accuracy and interpretability. On the other hand, we propose an evolutionary multi-objective approach for fuzzy classification from data with real and discrete attributes. The multi-objective evolutionary approach has been evaluated by means of three different evolutionary schemes: Preselection with niches, NSGA-II and ENORA. The results have been compared in terms of effectiveness by means of statistical techniques using the well-known standard Iris data set.

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Jiménez F, Sánchez G, Sánchez JF, Alcaraz JM. Fuzzy classification with multi-objective evolutionary algorithms. In Hybrid Artificial Intelligence Systems - Third International Workshop, HAIS 2008, Proceedings. 2008. p. 730-738. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-87656-4_90

Fuzzy classification with multi-objective evolutionary algorithms

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