Decision function estimation using intelligent gravitational search algorithm

Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Askari, Hossein [verfasserIn] Zahiri, Seyed-Hamid

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Gravitational search algorithm Fuzzy system Decision function Classifier

Anmerkung:	© Springer-Verlag 2011

Übergeordnetes Werk:	Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 3(2011), 2 vom: 07. Okt., Seite 163-172
Übergeordnetes Werk:	volume:3 ; year:2011 ; number:2 ; day:07 ; month:10 ; pages:163-172

Links:	Volltext

DOI / URN:	10.1007/s13042-011-0052-x

Katalog-ID:	SPR029598338

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allfields	10.1007/s13042-011-0052-x doi (DE-627)SPR029598338 (SPR)s13042-011-0052-x-e DE-627 ger DE-627 rakwb eng Askari, Hossein verfasserin aut Decision function estimation using intelligent gravitational search algorithm 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213 Zahiri, Seyed-Hamid aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 3(2011), 2 vom: 07. Okt., Seite 163-172 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:3 year:2011 number:2 day:07 month:10 pages:163-172 https://dx.doi.org/10.1007/s13042-011-0052-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2011 2 07 10 163-172
spelling	10.1007/s13042-011-0052-x doi (DE-627)SPR029598338 (SPR)s13042-011-0052-x-e DE-627 ger DE-627 rakwb eng Askari, Hossein verfasserin aut Decision function estimation using intelligent gravitational search algorithm 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213 Zahiri, Seyed-Hamid aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 3(2011), 2 vom: 07. Okt., Seite 163-172 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:3 year:2011 number:2 day:07 month:10 pages:163-172 https://dx.doi.org/10.1007/s13042-011-0052-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2011 2 07 10 163-172
allfields_unstemmed	10.1007/s13042-011-0052-x doi (DE-627)SPR029598338 (SPR)s13042-011-0052-x-e DE-627 ger DE-627 rakwb eng Askari, Hossein verfasserin aut Decision function estimation using intelligent gravitational search algorithm 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213 Zahiri, Seyed-Hamid aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 3(2011), 2 vom: 07. Okt., Seite 163-172 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:3 year:2011 number:2 day:07 month:10 pages:163-172 https://dx.doi.org/10.1007/s13042-011-0052-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2011 2 07 10 163-172
allfieldsGer	10.1007/s13042-011-0052-x doi (DE-627)SPR029598338 (SPR)s13042-011-0052-x-e DE-627 ger DE-627 rakwb eng Askari, Hossein verfasserin aut Decision function estimation using intelligent gravitational search algorithm 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213 Zahiri, Seyed-Hamid aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 3(2011), 2 vom: 07. Okt., Seite 163-172 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:3 year:2011 number:2 day:07 month:10 pages:163-172 https://dx.doi.org/10.1007/s13042-011-0052-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2011 2 07 10 163-172
allfieldsSound	10.1007/s13042-011-0052-x doi (DE-627)SPR029598338 (SPR)s13042-011-0052-x-e DE-627 ger DE-627 rakwb eng Askari, Hossein verfasserin aut Decision function estimation using intelligent gravitational search algorithm 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213 Zahiri, Seyed-Hamid aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 3(2011), 2 vom: 07. Okt., Seite 163-172 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:3 year:2011 number:2 day:07 month:10 pages:163-172 https://dx.doi.org/10.1007/s13042-011-0052-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 3 2011 2 07 10 163-172
language	English
source	Enthalten in International journal of machine learning and cybernetics 3(2011), 2 vom: 07. Okt., Seite 163-172 volume:3 year:2011 number:2 day:07 month:10 pages:163-172
sourceStr	Enthalten in International journal of machine learning and cybernetics 3(2011), 2 vom: 07. Okt., Seite 163-172 volume:3 year:2011 number:2 day:07 month:10 pages:163-172
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gravitational search algorithm Fuzzy system Decision function Classifier
isfreeaccess_bool	false
container_title	International journal of machine learning and cybernetics
authorswithroles_txt_mv	Askari, Hossein @@aut@@ Zahiri, Seyed-Hamid @@aut@@
publishDateDaySort_date	2011-10-07T00:00:00Z
hierarchy_top_id	635135132
id	SPR029598338
language_de	englisch
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author	Askari, Hossein
spellingShingle	Askari, Hossein misc Gravitational search algorithm misc Fuzzy system misc Decision function misc Classifier Decision function estimation using intelligent gravitational search algorithm
authorStr	Askari, Hossein
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topic_title	Decision function estimation using intelligent gravitational search algorithm Gravitational search algorithm (dpeaa)DE-He213 Fuzzy system (dpeaa)DE-He213 Decision function (dpeaa)DE-He213 Classifier (dpeaa)DE-He213
topic	misc Gravitational search algorithm misc Fuzzy system misc Decision function misc Classifier
topic_unstemmed	misc Gravitational search algorithm misc Fuzzy system misc Decision function misc Classifier
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title	Decision function estimation using intelligent gravitational search algorithm
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title_full	Decision function estimation using intelligent gravitational search algorithm
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author_browse	Askari, Hossein Zahiri, Seyed-Hamid
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doi_str_mv	10.1007/s13042-011-0052-x
title_sort	decision function estimation using intelligent gravitational search algorithm
title_auth	Decision function estimation using intelligent gravitational search algorithm
abstract	Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. © Springer-Verlag 2011
abstractGer	Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. © Springer-Verlag 2011
abstract_unstemmed	Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers. © Springer-Verlag 2011
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title_short	Decision function estimation using intelligent gravitational search algorithm
url	https://dx.doi.org/10.1007/s13042-011-0052-x
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up_date	2024-07-04T01:38:22.603Z
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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">SPR029598338</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230331104644.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2011 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13042-011-0052-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR029598338</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13042-011-0052-x-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">Askari, Hossein</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Decision function estimation using intelligent gravitational search algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</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="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2011</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gravitational search algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Decision function</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classifier</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zahiri, Seyed-Hamid</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of machine learning and cybernetics</subfield><subfield code="d">Heidelberg : Springer, 2010</subfield><subfield code="g">3(2011), 2 vom: 07. Okt., Seite 163-172</subfield><subfield code="w">(DE-627)635135132</subfield><subfield code="w">(DE-600)2572473-3</subfield><subfield code="x">1868-808X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:2</subfield><subfield code="g">day:07</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:163-172</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13042-011-0052-x</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="912" 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Decision function estimation using intelligent gravitational search algorithm

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