Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach
Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchic...
Ausführliche Beschreibung
Autor*in: |
Naik, Azad [verfasserIn] Rangwala, Huzefa [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Journal of intelligent information systems - Dordrecht : Springer Science + Business Media B.V, 1992, 52(2018), 1 vom: 18. Mai, Seite 141-164 |
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Übergeordnetes Werk: |
volume:52 ; year:2018 ; number:1 ; day:18 ; month:05 ; pages:141-164 |
Links: |
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DOI / URN: |
10.1007/s10844-018-0509-4 |
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Katalog-ID: |
SPR013673157 |
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520 | |a Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ | ||
650 | 4 | |a Top-down hierarchical classification |7 (dpeaa)DE-He213 | |
650 | 4 | |a Inconsistency |7 (dpeaa)DE-He213 | |
650 | 4 | |a Error propagation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Flattening |7 (dpeaa)DE-He213 | |
650 | 4 | |a Clustering |7 (dpeaa)DE-He213 | |
650 | 4 | |a Rewiring |7 (dpeaa)DE-He213 | |
700 | 1 | |a Rangwala, Huzefa |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of intelligent information systems |d Dordrecht : Springer Science + Business Media B.V, 1992 |g 52(2018), 1 vom: 18. Mai, Seite 141-164 |w (DE-627)269539131 |w (DE-600)1475525-7 |x 1573-7675 |7 nnns |
773 | 1 | 8 | |g volume:52 |g year:2018 |g number:1 |g day:18 |g month:05 |g pages:141-164 |
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2018 |
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publishDate |
2018 |
allfields |
10.1007/s10844-018-0509-4 doi (DE-627)SPR013673157 (SPR)s10844-018-0509-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Naik, Azad verfasserin aut Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 Rangwala, Huzefa verfasserin aut Enthalten in Journal of intelligent information systems Dordrecht : Springer Science + Business Media B.V, 1992 52(2018), 1 vom: 18. Mai, Seite 141-164 (DE-627)269539131 (DE-600)1475525-7 1573-7675 nnns volume:52 year:2018 number:1 day:18 month:05 pages:141-164 https://dx.doi.org/10.1007/s10844-018-0509-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_152 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 54.64 ASE 06.74 ASE 54.72 ASE AR 52 2018 1 18 05 141-164 |
spelling |
10.1007/s10844-018-0509-4 doi (DE-627)SPR013673157 (SPR)s10844-018-0509-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Naik, Azad verfasserin aut Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 Rangwala, Huzefa verfasserin aut Enthalten in Journal of intelligent information systems Dordrecht : Springer Science + Business Media B.V, 1992 52(2018), 1 vom: 18. Mai, Seite 141-164 (DE-627)269539131 (DE-600)1475525-7 1573-7675 nnns volume:52 year:2018 number:1 day:18 month:05 pages:141-164 https://dx.doi.org/10.1007/s10844-018-0509-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_152 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 54.64 ASE 06.74 ASE 54.72 ASE AR 52 2018 1 18 05 141-164 |
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10.1007/s10844-018-0509-4 doi (DE-627)SPR013673157 (SPR)s10844-018-0509-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Naik, Azad verfasserin aut Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 Rangwala, Huzefa verfasserin aut Enthalten in Journal of intelligent information systems Dordrecht : Springer Science + Business Media B.V, 1992 52(2018), 1 vom: 18. Mai, Seite 141-164 (DE-627)269539131 (DE-600)1475525-7 1573-7675 nnns volume:52 year:2018 number:1 day:18 month:05 pages:141-164 https://dx.doi.org/10.1007/s10844-018-0509-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_152 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 54.64 ASE 06.74 ASE 54.72 ASE AR 52 2018 1 18 05 141-164 |
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10.1007/s10844-018-0509-4 doi (DE-627)SPR013673157 (SPR)s10844-018-0509-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Naik, Azad verfasserin aut Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 Rangwala, Huzefa verfasserin aut Enthalten in Journal of intelligent information systems Dordrecht : Springer Science + Business Media B.V, 1992 52(2018), 1 vom: 18. Mai, Seite 141-164 (DE-627)269539131 (DE-600)1475525-7 1573-7675 nnns volume:52 year:2018 number:1 day:18 month:05 pages:141-164 https://dx.doi.org/10.1007/s10844-018-0509-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_152 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 54.64 ASE 06.74 ASE 54.72 ASE AR 52 2018 1 18 05 141-164 |
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10.1007/s10844-018-0509-4 doi (DE-627)SPR013673157 (SPR)s10844-018-0509-4-e DE-627 ger DE-627 rakwb eng 004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Naik, Azad verfasserin aut Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 Rangwala, Huzefa verfasserin aut Enthalten in Journal of intelligent information systems Dordrecht : Springer Science + Business Media B.V, 1992 52(2018), 1 vom: 18. Mai, Seite 141-164 (DE-627)269539131 (DE-600)1475525-7 1573-7675 nnns volume:52 year:2018 number:1 day:18 month:05 pages:141-164 https://dx.doi.org/10.1007/s10844-018-0509-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE 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_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_152 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 54.64 ASE 06.74 ASE 54.72 ASE AR 52 2018 1 18 05 141-164 |
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Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. 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Naik, Azad |
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Naik, Azad ddc 004 bkl 54.64 bkl 06.74 bkl 54.72 misc Top-down hierarchical classification misc Inconsistency misc Error propagation misc Flattening misc Clustering misc Rewiring Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach |
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004 ASE 54.64 bkl 06.74 bkl 54.72 bkl Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach Top-down hierarchical classification (dpeaa)DE-He213 Inconsistency (dpeaa)DE-He213 Error propagation (dpeaa)DE-He213 Flattening (dpeaa)DE-He213 Clustering (dpeaa)DE-He213 Rewiring (dpeaa)DE-He213 |
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improving large-scale hierarchical classification by rewiring: a data-driven filter based approach |
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Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach |
abstract |
Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ |
abstractGer |
Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ |
abstract_unstemmed |
Abstract Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/ |
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title_short |
Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach |
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https://dx.doi.org/10.1007/s10844-018-0509-4 |
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Rangwala, Huzefa |
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Rangwala, Huzefa |
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10.1007/s10844-018-0509-4 |
up_date |
2024-07-03T21:23:08.167Z |
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score |
7.398576 |