Stochastic correlation coefficient ensembles for variable selection
In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VS...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Che, JinXing [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of applied statistics - Abingdon [u.a.] : Taylor & Francis, 1984, 44(2017), 10, Seite 1721-22 |
|---|---|
| Übergeordnetes Werk: |
volume:44 ; year:2017 ; number:10 ; pages:1721-22 |
| Links: |
|---|
| DOI / URN: |
10.1080/02664763.2016.1221913 |
|---|
| Katalog-ID: |
OLC1994154357 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1994154357 | ||
| 003 | DE-627 | ||
| 005 | 20230715052516.0 | ||
| 007 | tu | ||
| 008 | 170721s2017 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/02664763.2016.1221913 |2 doi | |
| 028 | 5 | 2 | |a PQ20171228 |
| 035 | |a (DE-627)OLC1994154357 | ||
| 035 | |a (DE-599)GBVOLC1994154357 | ||
| 035 | |a (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 | ||
| 035 | |a (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q DNB |
| 100 | 1 | |a Che, JinXing |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Stochastic correlation coefficient ensembles for variable selection |
| 264 | 1 | |c 2017 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. | ||
| 540 | |a Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 | ||
| 650 | 4 | |a maximal relevance | |
| 650 | 4 | |a stochastic correlation coefficient ensemble | |
| 650 | 4 | |a ranking | |
| 650 | 4 | |a minimal redundancy | |
| 650 | 4 | |a LASSO | |
| 650 | 4 | |a Variables | |
| 650 | 4 | |a Computer simulation | |
| 650 | 4 | |a Stochastic models | |
| 650 | 4 | |a Data sets | |
| 650 | 4 | |a Probability theory | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Redundancy | |
| 650 | 4 | |a Correlation | |
| 650 | 4 | |a Criteria | |
| 650 | 4 | |a Regression coefficients | |
| 700 | 1 | |a Yang, YouLong |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of applied statistics |d Abingdon [u.a.] : Taylor & Francis, 1984 |g 44(2017), 10, Seite 1721-22 |w (DE-627)130678848 |w (DE-600)882603-1 |w (DE-576)016221605 |x 0266-4763 |7 nnns |
| 773 | 1 | 8 | |g volume:44 |g year:2017 |g number:10 |g pages:1721-22 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/02664763.2016.1221913 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 |
| 856 | 4 | 2 | |u https://search.proquest.com/docview/1905676814 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a GBV_ILN_70 | ||
| 951 | |a AR | ||
| 952 | |d 44 |j 2017 |e 10 |h 1721-22 | ||
| author_variant |
j c jc |
|---|---|
| matchkey_str |
article:02664763:2017----::tcatcorltocefcetnebefr |
| hierarchy_sort_str |
2017 |
| publishDate |
2017 |
| allfields |
10.1080/02664763.2016.1221913 doi PQ20171228 (DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab DE-627 ger DE-627 rakwb eng 510 DNB Che, JinXing verfasserin aut Stochastic correlation coefficient ensembles for variable selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients Yang, YouLong oth Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 10, Seite 1721-22 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:10 pages:1721-22 http://dx.doi.org/10.1080/02664763.2016.1221913 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 10 1721-22 |
| spelling |
10.1080/02664763.2016.1221913 doi PQ20171228 (DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab DE-627 ger DE-627 rakwb eng 510 DNB Che, JinXing verfasserin aut Stochastic correlation coefficient ensembles for variable selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients Yang, YouLong oth Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 10, Seite 1721-22 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:10 pages:1721-22 http://dx.doi.org/10.1080/02664763.2016.1221913 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 10 1721-22 |
| allfields_unstemmed |
10.1080/02664763.2016.1221913 doi PQ20171228 (DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab DE-627 ger DE-627 rakwb eng 510 DNB Che, JinXing verfasserin aut Stochastic correlation coefficient ensembles for variable selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients Yang, YouLong oth Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 10, Seite 1721-22 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:10 pages:1721-22 http://dx.doi.org/10.1080/02664763.2016.1221913 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 10 1721-22 |
| allfieldsGer |
10.1080/02664763.2016.1221913 doi PQ20171228 (DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab DE-627 ger DE-627 rakwb eng 510 DNB Che, JinXing verfasserin aut Stochastic correlation coefficient ensembles for variable selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients Yang, YouLong oth Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 10, Seite 1721-22 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:10 pages:1721-22 http://dx.doi.org/10.1080/02664763.2016.1221913 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 10 1721-22 |
| allfieldsSound |
10.1080/02664763.2016.1221913 doi PQ20171228 (DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab DE-627 ger DE-627 rakwb eng 510 DNB Che, JinXing verfasserin aut Stochastic correlation coefficient ensembles for variable selection 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016 maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients Yang, YouLong oth Enthalten in Journal of applied statistics Abingdon [u.a.] : Taylor & Francis, 1984 44(2017), 10, Seite 1721-22 (DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 0266-4763 nnns volume:44 year:2017 number:10 pages:1721-22 http://dx.doi.org/10.1080/02664763.2016.1221913 Volltext http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 44 2017 10 1721-22 |
| language |
English |
| source |
Enthalten in Journal of applied statistics 44(2017), 10, Seite 1721-22 volume:44 year:2017 number:10 pages:1721-22 |
| sourceStr |
Enthalten in Journal of applied statistics 44(2017), 10, Seite 1721-22 volume:44 year:2017 number:10 pages:1721-22 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Journal of applied statistics |
| authorswithroles_txt_mv |
Che, JinXing @@aut@@ Yang, YouLong @@oth@@ |
| publishDateDaySort_date |
2017-01-01T00:00:00Z |
| hierarchy_top_id |
130678848 |
| dewey-sort |
3510 |
| id |
OLC1994154357 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1994154357</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715052516.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170721s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/02664763.2016.1221913</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171228</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1994154357</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1994154357</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab</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">510</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Che, JinXing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stochastic correlation coefficient ensembles for variable selection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maximal relevance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stochastic correlation coefficient ensemble</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ranking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">minimal redundancy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LASSO</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variables</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probability theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Redundancy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Correlation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Criteria</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression coefficients</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, YouLong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of applied statistics</subfield><subfield code="d">Abingdon [u.a.] : Taylor & Francis, 1984</subfield><subfield code="g">44(2017), 10, Seite 1721-22</subfield><subfield code="w">(DE-627)130678848</subfield><subfield code="w">(DE-600)882603-1</subfield><subfield code="w">(DE-576)016221605</subfield><subfield code="x">0266-4763</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:44</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:10</subfield><subfield code="g">pages:1721-22</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/02664763.2016.1221913</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://search.proquest.com/docview/1905676814</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">44</subfield><subfield code="j">2017</subfield><subfield code="e">10</subfield><subfield code="h">1721-22</subfield></datafield></record></collection>
|
| author |
Che, JinXing |
| spellingShingle |
Che, JinXing ddc 510 misc maximal relevance misc stochastic correlation coefficient ensemble misc ranking misc minimal redundancy misc LASSO misc Variables misc Computer simulation misc Stochastic models misc Data sets misc Probability theory misc Algorithms misc Regression analysis misc Redundancy misc Correlation misc Criteria misc Regression coefficients Stochastic correlation coefficient ensembles for variable selection |
| authorStr |
Che, JinXing |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130678848 |
| format |
Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0266-4763 |
| topic_title |
510 DNB Stochastic correlation coefficient ensembles for variable selection maximal relevance stochastic correlation coefficient ensemble ranking minimal redundancy LASSO Variables Computer simulation Stochastic models Data sets Probability theory Algorithms Regression analysis Redundancy Correlation Criteria Regression coefficients |
| topic |
ddc 510 misc maximal relevance misc stochastic correlation coefficient ensemble misc ranking misc minimal redundancy misc LASSO misc Variables misc Computer simulation misc Stochastic models misc Data sets misc Probability theory misc Algorithms misc Regression analysis misc Redundancy misc Correlation misc Criteria misc Regression coefficients |
| topic_unstemmed |
ddc 510 misc maximal relevance misc stochastic correlation coefficient ensemble misc ranking misc minimal redundancy misc LASSO misc Variables misc Computer simulation misc Stochastic models misc Data sets misc Probability theory misc Algorithms misc Regression analysis misc Redundancy misc Correlation misc Criteria misc Regression coefficients |
| topic_browse |
ddc 510 misc maximal relevance misc stochastic correlation coefficient ensemble misc ranking misc minimal redundancy misc LASSO misc Variables misc Computer simulation misc Stochastic models misc Data sets misc Probability theory misc Algorithms misc Regression analysis misc Redundancy misc Correlation misc Criteria misc Regression coefficients |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
y y yy |
| hierarchy_parent_title |
Journal of applied statistics |
| hierarchy_parent_id |
130678848 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Journal of applied statistics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130678848 (DE-600)882603-1 (DE-576)016221605 |
| title |
Stochastic correlation coefficient ensembles for variable selection |
| ctrlnum |
(DE-627)OLC1994154357 (DE-599)GBVOLC1994154357 (PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840 (KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab |
| title_full |
Stochastic correlation coefficient ensembles for variable selection |
| author_sort |
Che, JinXing |
| journal |
Journal of applied statistics |
| journalStr |
Journal of applied statistics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2017 |
| contenttype_str_mv |
txt |
| container_start_page |
1721 |
| author_browse |
Che, JinXing |
| container_volume |
44 |
| class |
510 DNB |
| format_se |
Aufsätze |
| author-letter |
Che, JinXing |
| doi_str_mv |
10.1080/02664763.2016.1221913 |
| dewey-full |
510 |
| title_sort |
stochastic correlation coefficient ensembles for variable selection |
| title_auth |
Stochastic correlation coefficient ensembles for variable selection |
| abstract |
In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. |
| abstractGer |
In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. |
| abstract_unstemmed |
In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
10 |
| title_short |
Stochastic correlation coefficient ensembles for variable selection |
| url |
http://dx.doi.org/10.1080/02664763.2016.1221913 http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913 https://search.proquest.com/docview/1905676814 |
| remote_bool |
false |
| author2 |
Yang, YouLong |
| author2Str |
Yang, YouLong |
| ppnlink |
130678848 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1080/02664763.2016.1221913 |
| up_date |
2024-07-03T16:43:17.943Z |
| _version_ |
1803576932244127744 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1994154357</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715052516.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">170721s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/02664763.2016.1221913</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171228</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1994154357</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1994154357</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c2192-537ae07130c3862d7afcdd7127b6b62fc286ebbf0ae6ca2886e00456db18a6840</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0020036020170000044001001721stochasticcorrelationcoefficientensemblesforvariab</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">510</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Che, JinXing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Stochastic correlation coefficient ensembles for variable selection</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2016 Informa UK Limited, trading as Taylor & Francis Group 2016</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maximal relevance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">stochastic correlation coefficient ensemble</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ranking</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">minimal redundancy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">LASSO</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variables</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stochastic models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Probability theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Redundancy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Correlation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Criteria</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regression coefficients</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, YouLong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of applied statistics</subfield><subfield code="d">Abingdon [u.a.] : Taylor & Francis, 1984</subfield><subfield code="g">44(2017), 10, Seite 1721-22</subfield><subfield code="w">(DE-627)130678848</subfield><subfield code="w">(DE-600)882603-1</subfield><subfield code="w">(DE-576)016221605</subfield><subfield code="x">0266-4763</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:44</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:10</subfield><subfield code="g">pages:1721-22</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/02664763.2016.1221913</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1221913</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://search.proquest.com/docview/1905676814</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_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">44</subfield><subfield code="j">2017</subfield><subfield code="e">10</subfield><subfield code="h">1721-22</subfield></datafield></record></collection>
|
| score |
7.3990602 |