Evolutionary ensembles based on prioritized aggregation operator
Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Debnath, Chandrima [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 27(2023), 24 vom: 30. Okt., Seite 18469-18488 |
|---|---|
| Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:24 ; day:30 ; month:10 ; pages:18469-18488 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-023-09289-0 |
|---|
| Katalog-ID: |
SPR053716388 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR053716388 | ||
| 003 | DE-627 | ||
| 005 | 20231114064710.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231114s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-023-09289-0 |2 doi | |
| 035 | |a (DE-627)SPR053716388 | ||
| 035 | |a (SPR)s00500-023-09289-0-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Debnath, Chandrima |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Evolutionary ensembles based on prioritized aggregation operator |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. | ||
| 650 | 4 | |a Evolutionary algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Classifier ensemble |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Aggregation operator |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Averaging aggregation operators |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prioritized averaging operators |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Hait, Swati Rani |4 aut | |
| 700 | 1 | |a Guha, Debashree |4 aut | |
| 700 | 1 | |a Chakraborty, Debjani |0 (orcid)0000-0002-6929-6036 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 27(2023), 24 vom: 30. Okt., Seite 18469-18488 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2023 |g number:24 |g day:30 |g month:10 |g pages:18469-18488 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-023-09289-0 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 27 |j 2023 |e 24 |b 30 |c 10 |h 18469-18488 | ||
| author_variant |
c d cd s r h sr srh d g dg d c dc |
|---|---|
| matchkey_str |
debnathchandrimahaitswatiraniguhadebashr:2023----:vltoaynebebsdnroiieag |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s00500-023-09289-0 doi (DE-627)SPR053716388 (SPR)s00500-023-09289-0-e DE-627 ger DE-627 rakwb eng Debnath, Chandrima verfasserin aut Evolutionary ensembles based on prioritized aggregation operator 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 Hait, Swati Rani aut Guha, Debashree aut Chakraborty, Debjani (orcid)0000-0002-6929-6036 aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 24 vom: 30. Okt., Seite 18469-18488 (DE-627)SPR006469531 nnns volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 https://dx.doi.org/10.1007/s00500-023-09289-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 24 30 10 18469-18488 |
| spelling |
10.1007/s00500-023-09289-0 doi (DE-627)SPR053716388 (SPR)s00500-023-09289-0-e DE-627 ger DE-627 rakwb eng Debnath, Chandrima verfasserin aut Evolutionary ensembles based on prioritized aggregation operator 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 Hait, Swati Rani aut Guha, Debashree aut Chakraborty, Debjani (orcid)0000-0002-6929-6036 aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 24 vom: 30. Okt., Seite 18469-18488 (DE-627)SPR006469531 nnns volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 https://dx.doi.org/10.1007/s00500-023-09289-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 24 30 10 18469-18488 |
| allfields_unstemmed |
10.1007/s00500-023-09289-0 doi (DE-627)SPR053716388 (SPR)s00500-023-09289-0-e DE-627 ger DE-627 rakwb eng Debnath, Chandrima verfasserin aut Evolutionary ensembles based on prioritized aggregation operator 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 Hait, Swati Rani aut Guha, Debashree aut Chakraborty, Debjani (orcid)0000-0002-6929-6036 aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 24 vom: 30. Okt., Seite 18469-18488 (DE-627)SPR006469531 nnns volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 https://dx.doi.org/10.1007/s00500-023-09289-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 24 30 10 18469-18488 |
| allfieldsGer |
10.1007/s00500-023-09289-0 doi (DE-627)SPR053716388 (SPR)s00500-023-09289-0-e DE-627 ger DE-627 rakwb eng Debnath, Chandrima verfasserin aut Evolutionary ensembles based on prioritized aggregation operator 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 Hait, Swati Rani aut Guha, Debashree aut Chakraborty, Debjani (orcid)0000-0002-6929-6036 aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 24 vom: 30. Okt., Seite 18469-18488 (DE-627)SPR006469531 nnns volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 https://dx.doi.org/10.1007/s00500-023-09289-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 24 30 10 18469-18488 |
| allfieldsSound |
10.1007/s00500-023-09289-0 doi (DE-627)SPR053716388 (SPR)s00500-023-09289-0-e DE-627 ger DE-627 rakwb eng Debnath, Chandrima verfasserin aut Evolutionary ensembles based on prioritized aggregation operator 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 Hait, Swati Rani aut Guha, Debashree aut Chakraborty, Debjani (orcid)0000-0002-6929-6036 aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 24 vom: 30. Okt., Seite 18469-18488 (DE-627)SPR006469531 nnns volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 https://dx.doi.org/10.1007/s00500-023-09289-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 24 30 10 18469-18488 |
| language |
English |
| source |
Enthalten in Soft Computing 27(2023), 24 vom: 30. Okt., Seite 18469-18488 volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 |
| sourceStr |
Enthalten in Soft Computing 27(2023), 24 vom: 30. Okt., Seite 18469-18488 volume:27 year:2023 number:24 day:30 month:10 pages:18469-18488 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Evolutionary algorithm Classifier ensemble Aggregation operator Averaging aggregation operators Prioritized averaging operators |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Debnath, Chandrima @@aut@@ Hait, Swati Rani @@aut@@ Guha, Debashree @@aut@@ Chakraborty, Debjani @@aut@@ |
| publishDateDaySort_date |
2023-10-30T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR053716388 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053716388</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231114064710.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231114s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09289-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053716388</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09289-0-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">Debnath, Chandrima</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evolutionary ensembles based on prioritized aggregation operator</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classifier ensemble</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aggregation operator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Averaging aggregation operators</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prioritized averaging operators</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hait, Swati Rani</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guha, Debashree</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chakraborty, Debjani</subfield><subfield code="0">(orcid)0000-0002-6929-6036</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">27(2023), 24 vom: 30. Okt., Seite 18469-18488</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:27</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:24</subfield><subfield code="g">day:30</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:18469-18488</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09289-0</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">27</subfield><subfield code="j">2023</subfield><subfield code="e">24</subfield><subfield code="b">30</subfield><subfield code="c">10</subfield><subfield code="h">18469-18488</subfield></datafield></record></collection>
|
| author |
Debnath, Chandrima |
| spellingShingle |
Debnath, Chandrima misc Evolutionary algorithm misc Classifier ensemble misc Aggregation operator misc Averaging aggregation operators misc Prioritized averaging operators Evolutionary ensembles based on prioritized aggregation operator |
| authorStr |
Debnath, Chandrima |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Evolutionary ensembles based on prioritized aggregation operator Evolutionary algorithm (dpeaa)DE-He213 Classifier ensemble (dpeaa)DE-He213 Aggregation operator (dpeaa)DE-He213 Averaging aggregation operators (dpeaa)DE-He213 Prioritized averaging operators (dpeaa)DE-He213 |
| topic |
misc Evolutionary algorithm misc Classifier ensemble misc Aggregation operator misc Averaging aggregation operators misc Prioritized averaging operators |
| topic_unstemmed |
misc Evolutionary algorithm misc Classifier ensemble misc Aggregation operator misc Averaging aggregation operators misc Prioritized averaging operators |
| topic_browse |
misc Evolutionary algorithm misc Classifier ensemble misc Aggregation operator misc Averaging aggregation operators misc Prioritized averaging operators |
| 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 |
Evolutionary ensembles based on prioritized aggregation operator |
| ctrlnum |
(DE-627)SPR053716388 (SPR)s00500-023-09289-0-e |
| title_full |
Evolutionary ensembles based on prioritized aggregation operator |
| author_sort |
Debnath, Chandrima |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
18469 |
| author_browse |
Debnath, Chandrima Hait, Swati Rani Guha, Debashree Chakraborty, Debjani |
| container_volume |
27 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Debnath, Chandrima |
| doi_str_mv |
10.1007/s00500-023-09289-0 |
| normlink |
(ORCID)0000-0002-6929-6036 |
| normlink_prefix_str_mv |
(orcid)0000-0002-6929-6036 |
| title_sort |
evolutionary ensembles based on prioritized aggregation operator |
| title_auth |
Evolutionary ensembles based on prioritized aggregation operator |
| abstract |
Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
24 |
| title_short |
Evolutionary ensembles based on prioritized aggregation operator |
| url |
https://dx.doi.org/10.1007/s00500-023-09289-0 |
| remote_bool |
true |
| author2 |
Hait, Swati Rani Guha, Debashree Chakraborty, Debjani |
| author2Str |
Hait, Swati Rani Guha, Debashree Chakraborty, Debjani |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-023-09289-0 |
| up_date |
2024-07-03T21:31:09.700Z |
| _version_ |
1803595042996092928 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053716388</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231114064710.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231114s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09289-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053716388</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09289-0-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">Debnath, Chandrima</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Evolutionary ensembles based on prioritized aggregation operator</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Ensemble methods are advanced learning algorithm proposed for generating base classifiers and accumulating them all together to derive a new classifier which is expected to perform better than the constituent classifier. This study proposes a novel ensemble technique where a base learning classifier is trained repeatedly by using different weightings over the training samples or examples, and the process is governed by the conceptualization of evolutionary processes and the aggregation operators. We utilize the evolutionary technique that can efficiently search a large weighing space for enriching suitable weights (chromosome) to the training samples. For finding an appropriate weighting, the crossover and mutation processes are applied on the weighting space to get the optimized set of weights which is accomplished through different generations. The considered base learning classifier is trained over the training examples along with their respective weightings by utilizing a learning algorithm, and for the finite number of generations, the weights are evolved and optimized through the evolutionary process. All the classifiers obtained in different generations of the evolutionary process are utilized for efficiently building the final ensemble. The set of classifiers obtained in different generations are combined together by utilizing the concept of priority-based averaging aggregation operator by availing priority to different generations. The classifier ensemble is done with two forms of operators: one without priority degree and the other with the priority degree. The proposed classifier ensemble algorithm is tested over the UCI benchmark dataset. The results obtained through the experimental process are more accurate, consistent, and reliable while comparing to other state-of-the-art methods, which ensures the efficacy of the proposed algorithm.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Evolutionary algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classifier ensemble</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aggregation operator</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Averaging aggregation operators</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prioritized averaging operators</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hait, Swati Rani</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guha, Debashree</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chakraborty, Debjani</subfield><subfield code="0">(orcid)0000-0002-6929-6036</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">27(2023), 24 vom: 30. Okt., Seite 18469-18488</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:27</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:24</subfield><subfield code="g">day:30</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:18469-18488</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09289-0</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">27</subfield><subfield code="j">2023</subfield><subfield code="e">24</subfield><subfield code="b">30</subfield><subfield code="c">10</subfield><subfield code="h">18469-18488</subfield></datafield></record></collection>
|
| score |
7.4000893 |