A new approach for fuzzy classification by a multiple-attribute decision-making model
Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MA...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ranjbar, M. [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 26(2022), 9 vom: 17. März, Seite 4249-4260 |
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| Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:9 ; day:17 ; month:03 ; pages:4249-4260 |
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| DOI / URN: |
10.1007/s00500-022-06912-4 |
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| 520 | |a Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. | ||
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10.1007/s00500-022-06912-4 doi (DE-627)SPR046704272 (SPR)s00500-022-06912-4-e DE-627 ger DE-627 rakwb eng Ranjbar, M. verfasserin aut A new approach for fuzzy classification by a multiple-attribute decision-making model 2022 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 2022 Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. Fuzzy classification (dpeaa)DE-He213 MADM problem (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 Effati, S. aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 9 vom: 17. März, Seite 4249-4260 (DE-627)SPR006469531 nnns volume:26 year:2022 number:9 day:17 month:03 pages:4249-4260 https://dx.doi.org/10.1007/s00500-022-06912-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 9 17 03 4249-4260 |
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10.1007/s00500-022-06912-4 doi (DE-627)SPR046704272 (SPR)s00500-022-06912-4-e DE-627 ger DE-627 rakwb eng Ranjbar, M. verfasserin aut A new approach for fuzzy classification by a multiple-attribute decision-making model 2022 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 2022 Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. Fuzzy classification (dpeaa)DE-He213 MADM problem (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 Effati, S. aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 9 vom: 17. März, Seite 4249-4260 (DE-627)SPR006469531 nnns volume:26 year:2022 number:9 day:17 month:03 pages:4249-4260 https://dx.doi.org/10.1007/s00500-022-06912-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 9 17 03 4249-4260 |
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10.1007/s00500-022-06912-4 doi (DE-627)SPR046704272 (SPR)s00500-022-06912-4-e DE-627 ger DE-627 rakwb eng Ranjbar, M. verfasserin aut A new approach for fuzzy classification by a multiple-attribute decision-making model 2022 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 2022 Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. Fuzzy classification (dpeaa)DE-He213 MADM problem (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 Effati, S. aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 9 vom: 17. März, Seite 4249-4260 (DE-627)SPR006469531 nnns volume:26 year:2022 number:9 day:17 month:03 pages:4249-4260 https://dx.doi.org/10.1007/s00500-022-06912-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 9 17 03 4249-4260 |
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10.1007/s00500-022-06912-4 doi (DE-627)SPR046704272 (SPR)s00500-022-06912-4-e DE-627 ger DE-627 rakwb eng Ranjbar, M. verfasserin aut A new approach for fuzzy classification by a multiple-attribute decision-making model 2022 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 2022 Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. Fuzzy classification (dpeaa)DE-He213 MADM problem (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 Effati, S. aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 9 vom: 17. März, Seite 4249-4260 (DE-627)SPR006469531 nnns volume:26 year:2022 number:9 day:17 month:03 pages:4249-4260 https://dx.doi.org/10.1007/s00500-022-06912-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 9 17 03 4249-4260 |
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10.1007/s00500-022-06912-4 doi (DE-627)SPR046704272 (SPR)s00500-022-06912-4-e DE-627 ger DE-627 rakwb eng Ranjbar, M. verfasserin aut A new approach for fuzzy classification by a multiple-attribute decision-making model 2022 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 2022 Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. Fuzzy classification (dpeaa)DE-He213 MADM problem (dpeaa)DE-He213 TOPSIS method (dpeaa)DE-He213 Effati, S. aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 9 vom: 17. März, Seite 4249-4260 (DE-627)SPR006469531 nnns volume:26 year:2022 number:9 day:17 month:03 pages:4249-4260 https://dx.doi.org/10.1007/s00500-022-06912-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 9 17 03 4249-4260 |
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Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046704272</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507151607.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220409s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-022-06912-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046704272</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-022-06912-4-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">Ranjbar, M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A new approach for fuzzy classification by a multiple-attribute decision-making model</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we suggest a new classifier using a multiple-attribute decision-making (MADM) model for fuzzy classification. First, we form a decision-making matrix. Its elements are membership functions of a fuzzy set constructed by training datasets. Then, for any test data, we form an MADM problem, and by solving this problem with a method from the MADM techniques, we obtain a fuzzy classification. For this purpose, we utilize the technique for order of preference by similarity to ideal solution (TOPSIS) method as a well-known method in the MADM techniques. Additionally, we use a new criterion for determining a weight vector for features in this approach. We evaluate the obtained results of the new approach with five well-known algorithms on ten datasets. Also, we compare our new approach with the weightless algorithm and weighed algorithm by the generalized Fisher score in feature selection methods. Finally, to show the superiority of the new approach, we use a statistical comparison with other methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy classification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MADM problem</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">TOPSIS method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Effati, S.</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">26(2022), 9 vom: 17. März, Seite 4249-4260</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:26</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:9</subfield><subfield code="g">day:17</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:4249-4260</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-022-06912-4</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">26</subfield><subfield code="j">2022</subfield><subfield code="e">9</subfield><subfield code="b">17</subfield><subfield code="c">03</subfield><subfield code="h">4249-4260</subfield></datafield></record></collection>
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