Loading…
Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data
Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. Ho...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Conference Proceeding |
| Language: | English |
| Subjects: | |
| Online Access: | Request full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | |
| container_end_page | 216 |
| container_issue | |
| container_start_page | 212 |
| container_title | |
| container_volume | |
| creator | ZHANG, YONGQING LU, RONGZHAO HUANG, JI GAO, DONGRUI |
| description | Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods. |
| doi_str_mv | 10.1109/ICCWAMTIP47768.2019.9067647 |
| format | conference_proceeding |
| fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9067647</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9067647</ieee_id><sourcerecordid>9067647</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-cdb8b01c61b0242cd7081bde67b37e5602b803ba8923cdc3a10e5e96698a79c83</originalsourceid><addsrcrecordid>eNo1j01Lw0AYhFdFsNb8Ai8BzxvfdzfZj4tQ06iBioItHsvu5q1E8lGSKvjvDVhPc5hh5hnGbhASRLC3ZZ6_L57X5WuqtTKJALSJBaVVqk_YJWphMBWpkKdsJjKtuLEqPWOR1ebfQ3PBonH8BAApQGYoZuyu-O6br0Pdd2744fdupCouupFa31C86Soa-Jtr903dfcS7fojL1rvGdWGKLd3BXbHznWtGio46Z5uHYp0_8dXLY5kvVryehg48VN54wKDQw8QYKg0GfUVKe6kpUyC8AemdsUKGKkiHQBlZpaxx2gYj5-z6r7cmou1-qNsJd3u8L38B2B1NoA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data</title><source>IEEE Xplore All Conference Series</source><creator>ZHANG, YONGQING ; LU, RONGZHAO ; HUANG, JI ; GAO, DONGRUI</creator><creatorcontrib>ZHANG, YONGQING ; LU, RONGZHAO ; HUANG, JI ; GAO, DONGRUI</creatorcontrib><description>Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.</description><identifier>ISBN: 9781728142418</identifier><identifier>ISBN: 1728142415</identifier><identifier>EISSN: 2576-8964</identifier><identifier>EISBN: 1728142423</identifier><identifier>EISBN: 1728142407</identifier><identifier>EISBN: 9781728142425</identifier><identifier>EISBN: 9781728142401</identifier><identifier>DOI: 10.1109/ICCWAMTIP47768.2019.9067647</identifier><language>eng</language><publisher>IEEE</publisher><subject>Classification algorithms ; Ensemble learning ; Evolutionary algorithms ; Evolutionary computation ; Forestry ; Imbalanced data ; Information technology ; Machine learning ; Machine learning algorithms ; Sampling method ; Sampling methods</subject><ispartof>2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, 2019, p.212-216</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9067647$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9067647$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>ZHANG, YONGQING</creatorcontrib><creatorcontrib>LU, RONGZHAO</creatorcontrib><creatorcontrib>HUANG, JI</creatorcontrib><creatorcontrib>GAO, DONGRUI</creatorcontrib><title>Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data</title><title>2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing</title><addtitle>ICCWAMTIP</addtitle><description>Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.</description><subject>Classification algorithms</subject><subject>Ensemble learning</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Forestry</subject><subject>Imbalanced data</subject><subject>Information technology</subject><subject>Machine learning</subject><subject>Machine learning algorithms</subject><subject>Sampling method</subject><subject>Sampling methods</subject><issn>2576-8964</issn><isbn>9781728142418</isbn><isbn>1728142415</isbn><isbn>1728142423</isbn><isbn>1728142407</isbn><isbn>9781728142425</isbn><isbn>9781728142401</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2019</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1j01Lw0AYhFdFsNb8Ai8BzxvfdzfZj4tQ06iBioItHsvu5q1E8lGSKvjvDVhPc5hh5hnGbhASRLC3ZZ6_L57X5WuqtTKJALSJBaVVqk_YJWphMBWpkKdsJjKtuLEqPWOR1ebfQ3PBonH8BAApQGYoZuyu-O6br0Pdd2744fdupCouupFa31C86Soa-Jtr903dfcS7fojL1rvGdWGKLd3BXbHznWtGio46Z5uHYp0_8dXLY5kvVryehg48VN54wKDQw8QYKg0GfUVKe6kpUyC8AemdsUKGKkiHQBlZpaxx2gYj5-z6r7cmou1-qNsJd3u8L38B2B1NoA</recordid><startdate>201912</startdate><enddate>201912</enddate><creator>ZHANG, YONGQING</creator><creator>LU, RONGZHAO</creator><creator>HUANG, JI</creator><creator>GAO, DONGRUI</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201912</creationdate><title>Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data</title><author>ZHANG, YONGQING ; LU, RONGZHAO ; HUANG, JI ; GAO, DONGRUI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-cdb8b01c61b0242cd7081bde67b37e5602b803ba8923cdc3a10e5e96698a79c83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Classification algorithms</topic><topic>Ensemble learning</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Forestry</topic><topic>Imbalanced data</topic><topic>Information technology</topic><topic>Machine learning</topic><topic>Machine learning algorithms</topic><topic>Sampling method</topic><topic>Sampling methods</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHANG, YONGQING</creatorcontrib><creatorcontrib>LU, RONGZHAO</creatorcontrib><creatorcontrib>HUANG, JI</creatorcontrib><creatorcontrib>GAO, DONGRUI</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHANG, YONGQING</au><au>LU, RONGZHAO</au><au>HUANG, JI</au><au>GAO, DONGRUI</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data</atitle><btitle>2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing</btitle><stitle>ICCWAMTIP</stitle><date>2019-12</date><risdate>2019</risdate><spage>212</spage><epage>216</epage><pages>212-216</pages><eissn>2576-8964</eissn><isbn>9781728142418</isbn><isbn>1728142415</isbn><eisbn>1728142423</eisbn><eisbn>1728142407</eisbn><eisbn>9781728142425</eisbn><eisbn>9781728142401</eisbn><abstract>Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. The classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. Therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. The advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.</abstract><pub>IEEE</pub><doi>10.1109/ICCWAMTIP47768.2019.9067647</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781728142418 |
| ispartof | 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, 2019, p.212-216 |
| issn | 2576-8964 |
| language | eng |
| recordid | cdi_ieee_primary_9067647 |
| source | IEEE Xplore All Conference Series |
| subjects | Classification algorithms Ensemble learning Evolutionary algorithms Evolutionary computation Forestry Imbalanced data Information technology Machine learning Machine learning algorithms Sampling method Sampling methods |
| title | Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T14%3A48%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Evolutionary-Based%20Ensemble%20Under-Sampling%20for%20Imbalanced%20Data&rft.btitle=2019%2016th%20International%20Computer%20Conference%20on%20Wavelet%20Active%20Media%20Technology%20and%20Information%20Processing&rft.au=ZHANG,%20YONGQING&rft.date=2019-12&rft.spage=212&rft.epage=216&rft.pages=212-216&rft.eissn=2576-8964&rft.isbn=9781728142418&rft.isbn_list=1728142415&rft_id=info:doi/10.1109/ICCWAMTIP47768.2019.9067647&rft.eisbn=1728142423&rft.eisbn_list=1728142407&rft.eisbn_list=9781728142425&rft.eisbn_list=9781728142401&rft_dat=%3Cieee_CHZPO%3E9067647%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i203t-cdb8b01c61b0242cd7081bde67b37e5602b803ba8923cdc3a10e5e96698a79c83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9067647&rfr_iscdi=true |