Imbalanced data classification based on scaling kernel-based support vector machine
Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-c...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yong [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2014 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag London 2014 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 25(2014), 3-4 vom: 05. Apr., Seite 927-935 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2014 ; number:3-4 ; day:05 ; month:04 ; pages:927-935 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-014-1584-2 |
|---|
| Katalog-ID: |
OLC2025594216 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2025594216 | ||
| 003 | DE-627 | ||
| 005 | 20230502114607.0 | ||
| 007 | tu | ||
| 008 | 200820s2014 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00521-014-1584-2 |2 doi | |
| 035 | |a (DE-627)OLC2025594216 | ||
| 035 | |a (DE-He213)s00521-014-1584-2-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 Zhang, Yong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Imbalanced data classification based on scaling kernel-based support vector machine |
| 264 | 1 | |c 2014 | |
| 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-Verlag London 2014 | ||
| 520 | |a Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. | ||
| 650 | 4 | |a Imbalance data | |
| 650 | 4 | |a Scaling kernel | |
| 650 | 4 | |a Chi-square test | |
| 650 | 4 | |a Support vector machine | |
| 650 | 4 | |a Classification | |
| 700 | 1 | |a Fu, Panpan |4 aut | |
| 700 | 1 | |a Liu, Wenzhe |4 aut | |
| 700 | 1 | |a Chen, Guolong |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural computing & applications |d Springer London, 1993 |g 25(2014), 3-4 vom: 05. Apr., Seite 927-935 |w (DE-627)165669608 |w (DE-600)1136944-9 |w (DE-576)032873050 |x 0941-0643 |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2014 |g number:3-4 |g day:05 |g month:04 |g pages:927-935 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s00521-014-1584-2 |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 | ||
| 912 | |a GBV_ILN_2018 | ||
| 912 | |a GBV_ILN_4046 | ||
| 912 | |a GBV_ILN_4277 | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 2014 |e 3-4 |b 05 |c 04 |h 927-935 | ||
| author_variant |
y z yz p f pf w l wl g c gc |
|---|---|
| matchkey_str |
article:09410643:2014----::maacdaalsiiainaeosaigenlaes |
| hierarchy_sort_str |
2014 |
| publishDate |
2014 |
| allfields |
10.1007/s00521-014-1584-2 doi (DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Yong verfasserin aut Imbalanced data classification based on scaling kernel-based support vector machine 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. Imbalance data Scaling kernel Chi-square test Support vector machine Classification Fu, Panpan aut Liu, Wenzhe aut Chen, Guolong aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 05. Apr., Seite 927-935 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 https://doi.org/10.1007/s00521-014-1584-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 05 04 927-935 |
| spelling |
10.1007/s00521-014-1584-2 doi (DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Yong verfasserin aut Imbalanced data classification based on scaling kernel-based support vector machine 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. Imbalance data Scaling kernel Chi-square test Support vector machine Classification Fu, Panpan aut Liu, Wenzhe aut Chen, Guolong aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 05. Apr., Seite 927-935 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 https://doi.org/10.1007/s00521-014-1584-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 05 04 927-935 |
| allfields_unstemmed |
10.1007/s00521-014-1584-2 doi (DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Yong verfasserin aut Imbalanced data classification based on scaling kernel-based support vector machine 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. Imbalance data Scaling kernel Chi-square test Support vector machine Classification Fu, Panpan aut Liu, Wenzhe aut Chen, Guolong aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 05. Apr., Seite 927-935 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 https://doi.org/10.1007/s00521-014-1584-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 05 04 927-935 |
| allfieldsGer |
10.1007/s00521-014-1584-2 doi (DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Yong verfasserin aut Imbalanced data classification based on scaling kernel-based support vector machine 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. Imbalance data Scaling kernel Chi-square test Support vector machine Classification Fu, Panpan aut Liu, Wenzhe aut Chen, Guolong aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 05. Apr., Seite 927-935 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 https://doi.org/10.1007/s00521-014-1584-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 05 04 927-935 |
| allfieldsSound |
10.1007/s00521-014-1584-2 doi (DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p DE-627 ger DE-627 rakwb eng 004 VZ Zhang, Yong verfasserin aut Imbalanced data classification based on scaling kernel-based support vector machine 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. Imbalance data Scaling kernel Chi-square test Support vector machine Classification Fu, Panpan aut Liu, Wenzhe aut Chen, Guolong aut Enthalten in Neural computing & applications Springer London, 1993 25(2014), 3-4 vom: 05. Apr., Seite 927-935 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 https://doi.org/10.1007/s00521-014-1584-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 25 2014 3-4 05 04 927-935 |
| language |
English |
| source |
Enthalten in Neural computing & applications 25(2014), 3-4 vom: 05. Apr., Seite 927-935 volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 |
| sourceStr |
Enthalten in Neural computing & applications 25(2014), 3-4 vom: 05. Apr., Seite 927-935 volume:25 year:2014 number:3-4 day:05 month:04 pages:927-935 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Imbalance data Scaling kernel Chi-square test Support vector machine Classification |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Neural computing & applications |
| authorswithroles_txt_mv |
Zhang, Yong @@aut@@ Fu, Panpan @@aut@@ Liu, Wenzhe @@aut@@ Chen, Guolong @@aut@@ |
| publishDateDaySort_date |
2014-04-05T00:00:00Z |
| hierarchy_top_id |
165669608 |
| dewey-sort |
14 |
| id |
OLC2025594216 |
| 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">OLC2025594216</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114607.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-014-1584-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025594216</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-014-1584-2-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">Zhang, Yong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Imbalanced data classification based on scaling kernel-based support vector machine</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</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-Verlag London 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalance data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scaling kernel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chi-square test</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Support vector machine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Panpan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Wenzhe</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Guolong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">25(2014), 3-4 vom: 05. Apr., Seite 927-935</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:3-4</subfield><subfield code="g">day:05</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:927-935</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-014-1584-2</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="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2014</subfield><subfield code="e">3-4</subfield><subfield code="b">05</subfield><subfield code="c">04</subfield><subfield code="h">927-935</subfield></datafield></record></collection>
|
| author |
Zhang, Yong |
| spellingShingle |
Zhang, Yong ddc 004 misc Imbalance data misc Scaling kernel misc Chi-square test misc Support vector machine misc Classification Imbalanced data classification based on scaling kernel-based support vector machine |
| authorStr |
Zhang, Yong |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)165669608 |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0941-0643 |
| topic_title |
004 VZ Imbalanced data classification based on scaling kernel-based support vector machine Imbalance data Scaling kernel Chi-square test Support vector machine Classification |
| topic |
ddc 004 misc Imbalance data misc Scaling kernel misc Chi-square test misc Support vector machine misc Classification |
| topic_unstemmed |
ddc 004 misc Imbalance data misc Scaling kernel misc Chi-square test misc Support vector machine misc Classification |
| topic_browse |
ddc 004 misc Imbalance data misc Scaling kernel misc Chi-square test misc Support vector machine misc Classification |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Neural computing & applications |
| hierarchy_parent_id |
165669608 |
| dewey-tens |
000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Neural computing & applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 |
| title |
Imbalanced data classification based on scaling kernel-based support vector machine |
| ctrlnum |
(DE-627)OLC2025594216 (DE-He213)s00521-014-1584-2-p |
| title_full |
Imbalanced data classification based on scaling kernel-based support vector machine |
| author_sort |
Zhang, Yong |
| journal |
Neural computing & applications |
| journalStr |
Neural computing & applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2014 |
| contenttype_str_mv |
txt |
| container_start_page |
927 |
| author_browse |
Zhang, Yong Fu, Panpan Liu, Wenzhe Chen, Guolong |
| container_volume |
25 |
| class |
004 VZ |
| format_se |
Aufsätze |
| author-letter |
Zhang, Yong |
| doi_str_mv |
10.1007/s00521-014-1584-2 |
| dewey-full |
004 |
| title_sort |
imbalanced data classification based on scaling kernel-based support vector machine |
| title_auth |
Imbalanced data classification based on scaling kernel-based support vector machine |
| abstract |
Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. © Springer-Verlag London 2014 |
| abstractGer |
Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. © Springer-Verlag London 2014 |
| abstract_unstemmed |
Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization. © Springer-Verlag London 2014 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 |
| container_issue |
3-4 |
| title_short |
Imbalanced data classification based on scaling kernel-based support vector machine |
| url |
https://doi.org/10.1007/s00521-014-1584-2 |
| remote_bool |
false |
| author2 |
Fu, Panpan Liu, Wenzhe Chen, Guolong |
| author2Str |
Fu, Panpan Liu, Wenzhe Chen, Guolong |
| ppnlink |
165669608 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00521-014-1584-2 |
| up_date |
2024-07-04T01:38:49.151Z |
| _version_ |
1803610624256638976 |
| 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">OLC2025594216</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114607.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2014 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-014-1584-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025594216</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-014-1584-2-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">Zhang, Yong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Imbalanced data classification based on scaling kernel-based support vector machine</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</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-Verlag London 2014</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In many classification problems, the class distribution is imbalanced. Learning from the imbalance data is a remarkable challenge in the knowledge discovery and data mining field. In this paper, we propose a scaling kernel-based support vector machine (SVM) approach to deal with the multi-class imbalanced data classification problem. We first use standard SVM algorithm to gain an approximate hyperplane. Then, we present a scaling kernel function and calculate its parameters using the chi-square test and weighting factors. Experimental results on KEEL data sets show the proposed algorithm can resolve the classifier performance degradation problem due to data skewed distribution and has a good generalization.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imbalance data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Scaling kernel</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chi-square test</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Support vector machine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fu, Panpan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Wenzhe</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Guolong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">25(2014), 3-4 vom: 05. Apr., Seite 927-935</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:3-4</subfield><subfield code="g">day:05</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:927-935</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-014-1584-2</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="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2014</subfield><subfield code="e">3-4</subfield><subfield code="b">05</subfield><subfield code="c">04</subfield><subfield code="h">927-935</subfield></datafield></record></collection>
|
| score |
7.3989973 |