Joint neighborhood entropy-based gene selection method with fisher score for tumor classification
Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Tradit...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Lin [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Springer US, 1991, 49(2018), 4 vom: 03. Nov., Seite 1245-1259 |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2018 ; number:4 ; day:03 ; month:11 ; pages:1245-1259 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10489-018-1320-1 |
|---|
| Katalog-ID: |
OLC2066107336 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2066107336 | ||
| 003 | DE-627 | ||
| 005 | 20230502205036.0 | ||
| 007 | tu | ||
| 008 | 200820s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10489-018-1320-1 |2 doi | |
| 035 | |a (DE-627)OLC2066107336 | ||
| 035 | |a (DE-He213)s10489-018-1320-1-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |q VZ |
| 100 | 1 | |a Sun, Lin |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. | ||
| 650 | 4 | |a Rough sets | |
| 650 | 4 | |a Neighborhood rough sets | |
| 650 | 4 | |a Gene selection | |
| 650 | 4 | |a Neighborhood entropy | |
| 650 | 4 | |a Tumor classification | |
| 700 | 1 | |a Zhang, Xiao-Yu |4 aut | |
| 700 | 1 | |a Qian, Yu-Hua |4 aut | |
| 700 | 1 | |a Xu, Jiu-Cheng |0 (orcid)0000-0003-1518-3623 |4 aut | |
| 700 | 1 | |a Zhang, Shi-Guang |4 aut | |
| 700 | 1 | |a Tian, Yun |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied intelligence |d Springer US, 1991 |g 49(2018), 4 vom: 03. Nov., Seite 1245-1259 |w (DE-627)130990515 |w (DE-600)1080229-0 |w (DE-576)029154286 |x 0924-669X |7 nnns |
| 773 | 1 | 8 | |g volume:49 |g year:2018 |g number:4 |g day:03 |g month:11 |g pages:1245-1259 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10489-018-1320-1 |z lizenzpflichtig |3 Volltext |
| 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 49 |j 2018 |e 4 |b 03 |c 11 |h 1245-1259 | ||
| author_variant |
l s ls x y z xyz y h q yhq j c x jcx s g z sgz y t yt |
|---|---|
| matchkey_str |
article:0924669X:2018----::onnihohoetoyaegnslcinehdihihrcr |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s10489-018-1320-1 doi (DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Lin verfasserin aut Joint neighborhood entropy-based gene selection method with fisher score for tumor classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification Zhang, Xiao-Yu aut Qian, Yu-Hua aut Xu, Jiu-Cheng (orcid)0000-0003-1518-3623 aut Zhang, Shi-Guang aut Tian, Yun aut Enthalten in Applied intelligence Springer US, 1991 49(2018), 4 vom: 03. Nov., Seite 1245-1259 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 https://doi.org/10.1007/s10489-018-1320-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 49 2018 4 03 11 1245-1259 |
| spelling |
10.1007/s10489-018-1320-1 doi (DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Lin verfasserin aut Joint neighborhood entropy-based gene selection method with fisher score for tumor classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification Zhang, Xiao-Yu aut Qian, Yu-Hua aut Xu, Jiu-Cheng (orcid)0000-0003-1518-3623 aut Zhang, Shi-Guang aut Tian, Yun aut Enthalten in Applied intelligence Springer US, 1991 49(2018), 4 vom: 03. Nov., Seite 1245-1259 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 https://doi.org/10.1007/s10489-018-1320-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 49 2018 4 03 11 1245-1259 |
| allfields_unstemmed |
10.1007/s10489-018-1320-1 doi (DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Lin verfasserin aut Joint neighborhood entropy-based gene selection method with fisher score for tumor classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification Zhang, Xiao-Yu aut Qian, Yu-Hua aut Xu, Jiu-Cheng (orcid)0000-0003-1518-3623 aut Zhang, Shi-Guang aut Tian, Yun aut Enthalten in Applied intelligence Springer US, 1991 49(2018), 4 vom: 03. Nov., Seite 1245-1259 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 https://doi.org/10.1007/s10489-018-1320-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 49 2018 4 03 11 1245-1259 |
| allfieldsGer |
10.1007/s10489-018-1320-1 doi (DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Lin verfasserin aut Joint neighborhood entropy-based gene selection method with fisher score for tumor classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification Zhang, Xiao-Yu aut Qian, Yu-Hua aut Xu, Jiu-Cheng (orcid)0000-0003-1518-3623 aut Zhang, Shi-Guang aut Tian, Yun aut Enthalten in Applied intelligence Springer US, 1991 49(2018), 4 vom: 03. Nov., Seite 1245-1259 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 https://doi.org/10.1007/s10489-018-1320-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 49 2018 4 03 11 1245-1259 |
| allfieldsSound |
10.1007/s10489-018-1320-1 doi (DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p DE-627 ger DE-627 rakwb eng 004 VZ Sun, Lin verfasserin aut Joint neighborhood entropy-based gene selection method with fisher score for tumor classification 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification Zhang, Xiao-Yu aut Qian, Yu-Hua aut Xu, Jiu-Cheng (orcid)0000-0003-1518-3623 aut Zhang, Shi-Guang aut Tian, Yun aut Enthalten in Applied intelligence Springer US, 1991 49(2018), 4 vom: 03. Nov., Seite 1245-1259 (DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 0924-669X nnns volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 https://doi.org/10.1007/s10489-018-1320-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 49 2018 4 03 11 1245-1259 |
| language |
English |
| source |
Enthalten in Applied intelligence 49(2018), 4 vom: 03. Nov., Seite 1245-1259 volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 |
| sourceStr |
Enthalten in Applied intelligence 49(2018), 4 vom: 03. Nov., Seite 1245-1259 volume:49 year:2018 number:4 day:03 month:11 pages:1245-1259 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Applied intelligence |
| authorswithroles_txt_mv |
Sun, Lin @@aut@@ Zhang, Xiao-Yu @@aut@@ Qian, Yu-Hua @@aut@@ Xu, Jiu-Cheng @@aut@@ Zhang, Shi-Guang @@aut@@ Tian, Yun @@aut@@ |
| publishDateDaySort_date |
2018-11-03T00:00:00Z |
| hierarchy_top_id |
130990515 |
| dewey-sort |
14 |
| id |
OLC2066107336 |
| 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">OLC2066107336</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205036.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1320-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066107336</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1320-1-p</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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Joint neighborhood entropy-based gene selection method with fisher score for tumor classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rough sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neighborhood rough sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gene selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neighborhood entropy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tumor classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xiao-Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Yu-Hua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Jiu-Cheng</subfield><subfield code="0">(orcid)0000-0003-1518-3623</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Shi-Guang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Yun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">49(2018), 4 vom: 03. Nov., Seite 1245-1259</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:03</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1245-1259</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1320-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_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">49</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">03</subfield><subfield code="c">11</subfield><subfield code="h">1245-1259</subfield></datafield></record></collection>
|
| author |
Sun, Lin |
| spellingShingle |
Sun, Lin ddc 004 misc Rough sets misc Neighborhood rough sets misc Gene selection misc Neighborhood entropy misc Tumor classification Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| authorStr |
Sun, Lin |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)130990515 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0924-669X |
| topic_title |
004 VZ Joint neighborhood entropy-based gene selection method with fisher score for tumor classification Rough sets Neighborhood rough sets Gene selection Neighborhood entropy Tumor classification |
| topic |
ddc 004 misc Rough sets misc Neighborhood rough sets misc Gene selection misc Neighborhood entropy misc Tumor classification |
| topic_unstemmed |
ddc 004 misc Rough sets misc Neighborhood rough sets misc Gene selection misc Neighborhood entropy misc Tumor classification |
| topic_browse |
ddc 004 misc Rough sets misc Neighborhood rough sets misc Gene selection misc Neighborhood entropy misc Tumor classification |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Applied intelligence |
| hierarchy_parent_id |
130990515 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Applied intelligence |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)130990515 (DE-600)1080229-0 (DE-576)029154286 |
| title |
Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| ctrlnum |
(DE-627)OLC2066107336 (DE-He213)s10489-018-1320-1-p |
| title_full |
Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| author_sort |
Sun, Lin |
| journal |
Applied intelligence |
| journalStr |
Applied intelligence |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
1245 |
| author_browse |
Sun, Lin Zhang, Xiao-Yu Qian, Yu-Hua Xu, Jiu-Cheng Zhang, Shi-Guang Tian, Yun |
| container_volume |
49 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Sun, Lin |
| doi_str_mv |
10.1007/s10489-018-1320-1 |
| normlink |
(ORCID)0000-0003-1518-3623 |
| normlink_prefix_str_mv |
(orcid)0000-0003-1518-3623 |
| dewey-full |
004 |
| title_sort |
joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| title_auth |
Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| abstract |
Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 |
| container_issue |
4 |
| title_short |
Joint neighborhood entropy-based gene selection method with fisher score for tumor classification |
| url |
https://doi.org/10.1007/s10489-018-1320-1 |
| remote_bool |
false |
| author2 |
Zhang, Xiao-Yu Qian, Yu-Hua Xu, Jiu-Cheng Zhang, Shi-Guang Tian, Yun |
| author2Str |
Zhang, Xiao-Yu Qian, Yu-Hua Xu, Jiu-Cheng Zhang, Shi-Guang Tian, Yun |
| ppnlink |
130990515 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10489-018-1320-1 |
| up_date |
2024-07-04T03:46:57.796Z |
| _version_ |
1803618686393647104 |
| 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">OLC2066107336</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502205036.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-018-1320-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066107336</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10489-018-1320-1-p</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">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Joint neighborhood entropy-based gene selection method with fisher score for tumor classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Tumor classification is one of the most vital technologies for cancer diagnosis. Due to the high dimensionality, gene selection (finding a small, closely related gene set to accurately classify tumor) is an important step for improving gene expression data classification performance. Traditional rough set model as a classical attribute reduction method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, a novel neighborhood rough sets and entropy measure-based gene selection with Fisher score for tumor classification is proposed, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. First, the Fisher score method is employed to eliminate irrelevant genes to significantly reduce computation complexity. Next, some neighborhood entropy-based uncertainty measures are investigated for handling the uncertainty and noisy of gene expression data. Moreover, some of their properties are derived and the relationships among these measures are established. Finally, a joint neighborhood entropy-based gene selection algorithm with the Fisher score is presented to improve the classification performance of gene expression data. The experimental results under an instance and several public gene expression data sets prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rough sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neighborhood rough sets</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gene selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neighborhood entropy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tumor classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xiao-Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Yu-Hua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Jiu-Cheng</subfield><subfield code="0">(orcid)0000-0003-1518-3623</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Shi-Guang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Yun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Applied intelligence</subfield><subfield code="d">Springer US, 1991</subfield><subfield code="g">49(2018), 4 vom: 03. Nov., Seite 1245-1259</subfield><subfield code="w">(DE-627)130990515</subfield><subfield code="w">(DE-600)1080229-0</subfield><subfield code="w">(DE-576)029154286</subfield><subfield code="x">0924-669X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:4</subfield><subfield code="g">day:03</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1245-1259</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10489-018-1320-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_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">49</subfield><subfield code="j">2018</subfield><subfield code="e">4</subfield><subfield code="b">03</subfield><subfield code="c">11</subfield><subfield code="h">1245-1259</subfield></datafield></record></collection>
|
| score |
7.4018183 |