Improving predictive performance on survival in dairy cattle using an ensemble learning approach
• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in hi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
van der Heide, E.M.M. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth - Jia, Linrui ELSEVIER, 2022, COMPAG online : an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:177 ; year:2020 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.compag.2020.105675 |
|---|
| Katalog-ID: |
ELV051286483 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV051286483 | ||
| 003 | DE-627 | ||
| 005 | 20230624173036.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210910s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.compag.2020.105675 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica |
| 035 | |a (DE-627)ELV051286483 | ||
| 035 | |a (ELSEVIER)S0168-1699(20)30438-5 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 690 |q VZ |
| 084 | |a 52.43 |2 bkl | ||
| 084 | |a 52.52 |2 bkl | ||
| 084 | |a 52.42 |2 bkl | ||
| 084 | |a 50.38 |2 bkl | ||
| 100 | 1 | |a van der Heide, E.M.M. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| 264 | 1 | |c 2020 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. | ||
| 650 | 7 | |a Dairy cow |2 Elsevier | |
| 650 | 7 | |a Ensemble |2 Elsevier | |
| 650 | 7 | |a Machine learning |2 Elsevier | |
| 650 | 7 | |a Survival |2 Elsevier | |
| 700 | 1 | |a Kamphuis, C. |4 oth | |
| 700 | 1 | |a Veerkamp, R.F. |4 oth | |
| 700 | 1 | |a Athanasiadis, I.N. |4 oth | |
| 700 | 1 | |a Azzopardi, G. |4 oth | |
| 700 | 1 | |a van Pelt, M.L. |4 oth | |
| 700 | 1 | |a Ducro, B.J. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Jia, Linrui ELSEVIER |t Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |d 2022 |d COMPAG online : an international journal |g Amsterdam [u.a.] |w (DE-627)ELV008658315 |
| 773 | 1 | 8 | |g volume:177 |g year:2020 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.compag.2020.105675 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 52.43 |j Kältetechnik |q VZ |
| 936 | b | k | |a 52.52 |j Thermische Energieerzeugung |j Wärmetechnik |q VZ |
| 936 | b | k | |a 52.42 |j Heizungstechnik |j Lüftungstechnik |j Klimatechnik |q VZ |
| 936 | b | k | |a 50.38 |j Technische Thermodynamik |q VZ |
| 951 | |a AR | ||
| 952 | |d 177 |j 2020 |h 0 | ||
| author_variant |
d h e v dhe dhev |
|---|---|
| matchkey_str |
vanderheideemmkamphuiscveerkamprfathanas:2020----:mrvnpeitvpromnenuvvlnarctluignn |
| hierarchy_sort_str |
2020 |
| bklnumber |
52.43 52.52 52.42 50.38 |
| publishDate |
2020 |
| allfields |
10.1016/j.compag.2020.105675 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 DE-627 ger DE-627 rakwb eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl van der Heide, E.M.M. verfasserin aut Improving predictive performance on survival in dairy cattle using an ensemble learning approach 2020 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier Kamphuis, C. oth Veerkamp, R.F. oth Athanasiadis, I.N. oth Azzopardi, G. oth van Pelt, M.L. oth Ducro, B.J. oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:177 year:2020 pages:0 https://doi.org/10.1016/j.compag.2020.105675 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 177 2020 0 |
| spelling |
10.1016/j.compag.2020.105675 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 DE-627 ger DE-627 rakwb eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl van der Heide, E.M.M. verfasserin aut Improving predictive performance on survival in dairy cattle using an ensemble learning approach 2020 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier Kamphuis, C. oth Veerkamp, R.F. oth Athanasiadis, I.N. oth Azzopardi, G. oth van Pelt, M.L. oth Ducro, B.J. oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:177 year:2020 pages:0 https://doi.org/10.1016/j.compag.2020.105675 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 177 2020 0 |
| allfields_unstemmed |
10.1016/j.compag.2020.105675 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 DE-627 ger DE-627 rakwb eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl van der Heide, E.M.M. verfasserin aut Improving predictive performance on survival in dairy cattle using an ensemble learning approach 2020 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier Kamphuis, C. oth Veerkamp, R.F. oth Athanasiadis, I.N. oth Azzopardi, G. oth van Pelt, M.L. oth Ducro, B.J. oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:177 year:2020 pages:0 https://doi.org/10.1016/j.compag.2020.105675 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 177 2020 0 |
| allfieldsGer |
10.1016/j.compag.2020.105675 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 DE-627 ger DE-627 rakwb eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl van der Heide, E.M.M. verfasserin aut Improving predictive performance on survival in dairy cattle using an ensemble learning approach 2020 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier Kamphuis, C. oth Veerkamp, R.F. oth Athanasiadis, I.N. oth Azzopardi, G. oth van Pelt, M.L. oth Ducro, B.J. oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:177 year:2020 pages:0 https://doi.org/10.1016/j.compag.2020.105675 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 177 2020 0 |
| allfieldsSound |
10.1016/j.compag.2020.105675 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 DE-627 ger DE-627 rakwb eng 690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl van der Heide, E.M.M. verfasserin aut Improving predictive performance on survival in dairy cattle using an ensemble learning approach 2020 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier Kamphuis, C. oth Veerkamp, R.F. oth Athanasiadis, I.N. oth Azzopardi, G. oth van Pelt, M.L. oth Ducro, B.J. oth Enthalten in Elsevier Science Jia, Linrui ELSEVIER Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth 2022 COMPAG online : an international journal Amsterdam [u.a.] (DE-627)ELV008658315 volume:177 year:2020 pages:0 https://doi.org/10.1016/j.compag.2020.105675 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 52.43 Kältetechnik VZ 52.52 Thermische Energieerzeugung Wärmetechnik VZ 52.42 Heizungstechnik Lüftungstechnik Klimatechnik VZ 50.38 Technische Thermodynamik VZ AR 177 2020 0 |
| language |
English |
| source |
Enthalten in Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth Amsterdam [u.a.] volume:177 year:2020 pages:0 |
| sourceStr |
Enthalten in Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth Amsterdam [u.a.] volume:177 year:2020 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Kältetechnik Thermische Energieerzeugung Wärmetechnik Heizungstechnik Lüftungstechnik Klimatechnik Technische Thermodynamik |
| institution |
findex.gbv.de |
| topic_facet |
Dairy cow Ensemble Machine learning Survival |
| dewey-raw |
690 |
| isfreeaccess_bool |
false |
| container_title |
Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
| authorswithroles_txt_mv |
van der Heide, E.M.M. @@aut@@ Kamphuis, C. @@oth@@ Veerkamp, R.F. @@oth@@ Athanasiadis, I.N. @@oth@@ Azzopardi, G. @@oth@@ van Pelt, M.L. @@oth@@ Ducro, B.J. @@oth@@ |
| publishDateDaySort_date |
2020-01-01T00:00:00Z |
| hierarchy_top_id |
ELV008658315 |
| dewey-sort |
3690 |
| id |
ELV051286483 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV051286483</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624173036.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compag.2020.105675</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV051286483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0168-1699(20)30438-5</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="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.43</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.42</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.38</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">van der Heide, E.M.M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving predictive performance on survival in dairy cattle using an ensemble learning approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dairy cow</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ensemble</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Survival</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kamphuis, C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Veerkamp, R.F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Athanasiadis, I.N.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Azzopardi, G.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">van Pelt, M.L.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ducro, B.J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Jia, Linrui ELSEVIER</subfield><subfield code="t">Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth</subfield><subfield code="d">2022</subfield><subfield code="d">COMPAG online : an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV008658315</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:177</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.compag.2020.105675</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.43</subfield><subfield code="j">Kältetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.52</subfield><subfield code="j">Thermische Energieerzeugung</subfield><subfield code="j">Wärmetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.42</subfield><subfield code="j">Heizungstechnik</subfield><subfield code="j">Lüftungstechnik</subfield><subfield code="j">Klimatechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.38</subfield><subfield code="j">Technische Thermodynamik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">177</subfield><subfield code="j">2020</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
van der Heide, E.M.M. |
| spellingShingle |
van der Heide, E.M.M. ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 Elsevier Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| authorStr |
van der Heide, E.M.M. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV008658315 |
| format |
electronic Article |
| dewey-ones |
690 - Buildings |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl Improving predictive performance on survival in dairy cattle using an ensemble learning approach Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival Elsevier |
| topic |
ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 Elsevier Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival |
| topic_unstemmed |
ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 Elsevier Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival |
| topic_browse |
ddc 690 bkl 52.43 bkl 52.52 bkl 52.42 bkl 50.38 Elsevier Dairy cow Elsevier Ensemble Elsevier Machine learning Elsevier Survival |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
c k ck r v rv i a ia g a ga p m v pm pmv b d bd |
| hierarchy_parent_title |
Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
| hierarchy_parent_id |
ELV008658315 |
| dewey-tens |
690 - Building & construction |
| hierarchy_top_title |
Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV008658315 |
| title |
Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| ctrlnum |
(DE-627)ELV051286483 (ELSEVIER)S0168-1699(20)30438-5 |
| title_full |
Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| author_sort |
van der Heide, E.M.M. |
| journal |
Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
| journalStr |
Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
van der Heide, E.M.M. |
| container_volume |
177 |
| class |
690 VZ 52.43 bkl 52.52 bkl 52.42 bkl 50.38 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
van der Heide, E.M.M. |
| doi_str_mv |
10.1016/j.compag.2020.105675 |
| dewey-full |
690 |
| title_sort |
improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| title_auth |
Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| abstract |
• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. |
| abstractGer |
• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. |
| abstract_unstemmed |
• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Improving predictive performance on survival in dairy cattle using an ensemble learning approach |
| url |
https://doi.org/10.1016/j.compag.2020.105675 |
| remote_bool |
true |
| author2 |
Kamphuis, C. Veerkamp, R.F. Athanasiadis, I.N. Azzopardi, G. van Pelt, M.L. Ducro, B.J. |
| author2Str |
Kamphuis, C. Veerkamp, R.F. Athanasiadis, I.N. Azzopardi, G. van Pelt, M.L. Ducro, B.J. |
| ppnlink |
ELV008658315 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth |
| doi_str |
10.1016/j.compag.2020.105675 |
| up_date |
2024-07-06T19:51:07.233Z |
| _version_ |
1803860539851407360 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV051286483</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624173036.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.compag.2020.105675</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV051286483</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0168-1699(20)30438-5</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="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.43</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">52.42</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.38</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">van der Heide, E.M.M.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improving predictive performance on survival in dairy cattle using an ensemble learning approach</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• Ensemble methods can help improve prediction outcomes for complex traits. • Multiple regression and naive Bayes improve recall, AUC and balanced accuracy for some data sets. • Multiple logistic regression was the ensemble method with the best performance overall. • Continuous outcomes result in higher model performance than binary prediction outcomes.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Dairy cow</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ensemble</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Survival</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kamphuis, C.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Veerkamp, R.F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Athanasiadis, I.N.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Azzopardi, G.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">van Pelt, M.L.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ducro, B.J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Jia, Linrui ELSEVIER</subfield><subfield code="t">Analytical heat transfer model for coaxial heat exchangers based on varied heat flux with borehole depth</subfield><subfield code="d">2022</subfield><subfield code="d">COMPAG online : an international journal</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV008658315</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:177</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.compag.2020.105675</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.43</subfield><subfield code="j">Kältetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.52</subfield><subfield code="j">Thermische Energieerzeugung</subfield><subfield code="j">Wärmetechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.42</subfield><subfield code="j">Heizungstechnik</subfield><subfield code="j">Lüftungstechnik</subfield><subfield code="j">Klimatechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.38</subfield><subfield code="j">Technische Thermodynamik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">177</subfield><subfield code="j">2020</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.4001293 |