Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning
Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ishibuchi, Hisao [verfasserIn] Nakashima, Yusuke [verfasserIn] Nojima, Yusuke [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2010 |
|---|
| Schlagwörter: |
Fuzzy rule-based classification Genetics-based machine learning |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 15(2010), 12 vom: 17. Nov., Seite 2415-2434 |
|---|---|
| Übergeordnetes Werk: |
volume:15 ; year:2010 ; number:12 ; day:17 ; month:11 ; pages:2415-2434 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-010-0669-9 |
|---|
| Katalog-ID: |
SPR006478344 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR006478344 | ||
| 003 | DE-627 | ||
| 005 | 20201124002731.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201005s2010 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-010-0669-9 |2 doi | |
| 035 | |a (DE-627)SPR006478344 | ||
| 035 | |a (SPR)s00500-010-0669-9-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ishibuchi, Hisao |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| 264 | 1 | |c 2010 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. | ||
| 650 | 4 | |a Fuzzy rule-based classification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Genetic algorithms |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Genetics-based machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiobjective machine learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Evolutionary multiobjective optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nakashima, Yusuke |e verfasserin |4 aut | |
| 700 | 1 | |a Nojima, Yusuke |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 15(2010), 12 vom: 17. Nov., Seite 2415-2434 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:15 |g year:2010 |g number:12 |g day:17 |g month:11 |g pages:2415-2434 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-010-0669-9 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 15 |j 2010 |e 12 |b 17 |c 11 |h 2415-2434 | ||
| author_variant |
h i hi y n yn y n yn |
|---|---|
| matchkey_str |
ishibuchihisaonakashimayusukenojimayusuk:2010----:efraceautooeouinrmlibetvotmztoagrtmfrutojcieu |
| hierarchy_sort_str |
2010 |
| publishDate |
2010 |
| allfields |
10.1007/s00500-010-0669-9 doi (DE-627)SPR006478344 (SPR)s00500-010-0669-9-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 Nakashima, Yusuke verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 12 vom: 17. Nov., Seite 2415-2434 (DE-627)SPR006469531 nnns volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 https://dx.doi.org/10.1007/s00500-010-0669-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 12 17 11 2415-2434 |
| spelling |
10.1007/s00500-010-0669-9 doi (DE-627)SPR006478344 (SPR)s00500-010-0669-9-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 Nakashima, Yusuke verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 12 vom: 17. Nov., Seite 2415-2434 (DE-627)SPR006469531 nnns volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 https://dx.doi.org/10.1007/s00500-010-0669-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 12 17 11 2415-2434 |
| allfields_unstemmed |
10.1007/s00500-010-0669-9 doi (DE-627)SPR006478344 (SPR)s00500-010-0669-9-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 Nakashima, Yusuke verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 12 vom: 17. Nov., Seite 2415-2434 (DE-627)SPR006469531 nnns volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 https://dx.doi.org/10.1007/s00500-010-0669-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 12 17 11 2415-2434 |
| allfieldsGer |
10.1007/s00500-010-0669-9 doi (DE-627)SPR006478344 (SPR)s00500-010-0669-9-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 Nakashima, Yusuke verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 12 vom: 17. Nov., Seite 2415-2434 (DE-627)SPR006469531 nnns volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 https://dx.doi.org/10.1007/s00500-010-0669-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 12 17 11 2415-2434 |
| allfieldsSound |
10.1007/s00500-010-0669-9 doi (DE-627)SPR006478344 (SPR)s00500-010-0669-9-e DE-627 ger DE-627 rakwb eng Ishibuchi, Hisao verfasserin aut Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 Nakashima, Yusuke verfasserin aut Nojima, Yusuke verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 15(2010), 12 vom: 17. Nov., Seite 2415-2434 (DE-627)SPR006469531 nnns volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 https://dx.doi.org/10.1007/s00500-010-0669-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 15 2010 12 17 11 2415-2434 |
| language |
English |
| source |
Enthalten in Soft Computing 15(2010), 12 vom: 17. Nov., Seite 2415-2434 volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 |
| sourceStr |
Enthalten in Soft Computing 15(2010), 12 vom: 17. Nov., Seite 2415-2434 volume:15 year:2010 number:12 day:17 month:11 pages:2415-2434 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Fuzzy rule-based classification Genetic algorithms Genetics-based machine learning Multiobjective machine learning Evolutionary multiobjective optimization |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Ishibuchi, Hisao @@aut@@ Nakashima, Yusuke @@aut@@ Nojima, Yusuke @@aut@@ |
| publishDateDaySort_date |
2010-11-17T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR006478344 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006478344</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2010 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-010-0669-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006478344</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-010-0669-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ishibuchi, Hisao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy rule-based classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetics-based machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiobjective machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary multiobjective optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nakashima, Yusuke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nojima, Yusuke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">15(2010), 12 vom: 17. Nov., Seite 2415-2434</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:12</subfield><subfield code="g">day:17</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:2415-2434</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-010-0669-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2010</subfield><subfield code="e">12</subfield><subfield code="b">17</subfield><subfield code="c">11</subfield><subfield code="h">2415-2434</subfield></datafield></record></collection>
|
| author |
Ishibuchi, Hisao |
| spellingShingle |
Ishibuchi, Hisao misc Fuzzy rule-based classification misc Genetic algorithms misc Genetics-based machine learning misc Multiobjective machine learning misc Evolutionary multiobjective optimization Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| authorStr |
Ishibuchi, Hisao |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning Fuzzy rule-based classification (dpeaa)DE-He213 Genetic algorithms (dpeaa)DE-He213 Genetics-based machine learning (dpeaa)DE-He213 Multiobjective machine learning (dpeaa)DE-He213 Evolutionary multiobjective optimization (dpeaa)DE-He213 |
| topic |
misc Fuzzy rule-based classification misc Genetic algorithms misc Genetics-based machine learning misc Multiobjective machine learning misc Evolutionary multiobjective optimization |
| topic_unstemmed |
misc Fuzzy rule-based classification misc Genetic algorithms misc Genetics-based machine learning misc Multiobjective machine learning misc Evolutionary multiobjective optimization |
| topic_browse |
misc Fuzzy rule-based classification misc Genetic algorithms misc Genetics-based machine learning misc Multiobjective machine learning misc Evolutionary multiobjective optimization |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Soft Computing |
| hierarchy_parent_id |
SPR006469531 |
| hierarchy_top_title |
Soft Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR006469531 |
| title |
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| ctrlnum |
(DE-627)SPR006478344 (SPR)s00500-010-0669-9-e |
| title_full |
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| author_sort |
Ishibuchi, Hisao |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2010 |
| contenttype_str_mv |
txt |
| container_start_page |
2415 |
| author_browse |
Ishibuchi, Hisao Nakashima, Yusuke Nojima, Yusuke |
| container_volume |
15 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ishibuchi, Hisao |
| doi_str_mv |
10.1007/s00500-010-0669-9 |
| author2-role |
verfasserin |
| title_sort |
performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| title_auth |
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| abstract |
Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. |
| abstractGer |
Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. |
| abstract_unstemmed |
Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
12 |
| title_short |
Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning |
| url |
https://dx.doi.org/10.1007/s00500-010-0669-9 |
| remote_bool |
true |
| author2 |
Nakashima, Yusuke Nojima, Yusuke |
| author2Str |
Nakashima, Yusuke Nojima, Yusuke |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-010-0669-9 |
| up_date |
2024-07-03T23:13:35.049Z |
| _version_ |
1803601486858420224 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006478344</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2010 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-010-0669-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006478344</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-010-0669-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ishibuchi, Hisao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Performance evaluation of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Recently, evolutionary multiobjective optimization (EMO) algorithms have been utilized for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems (MoGFS), where EMO algorithms are used to search for non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. In this paper, we examine the ability of EMO algorithms to efficiently search for Pareto optimal or near Pareto optimal fuzzy rule-based systems for classification problems. We use NSGA-II (elitist non-dominated sorting genetic algorithm), its variants, and MOEA/D (multiobjective evolutionary algorithm based on decomposition) in our multiobjective fuzzy genetics-based machine learning (MoFGBML) algorithm. Classification performance of obtained fuzzy rule-based systems by each EMO algorithm is evaluated for training data and test data under various settings of the available computation load and the granularity of fuzzy partitions. Experimental results in this paper suggest that reported classification performance of MoGFS in the literature can be further improved using more computation load, more efficient EMO algorithms, and/or more antecedent fuzzy sets from finer fuzzy partitions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy rule-based classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetic algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Genetics-based machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiobjective machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary multiobjective optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nakashima, Yusuke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nojima, Yusuke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">15(2010), 12 vom: 17. Nov., Seite 2415-2434</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:15</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:12</subfield><subfield code="g">day:17</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:2415-2434</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-010-0669-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">15</subfield><subfield code="j">2010</subfield><subfield code="e">12</subfield><subfield code="b">17</subfield><subfield code="c">11</subfield><subfield code="h">2415-2434</subfield></datafield></record></collection>
|
| score |
7.401865 |