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A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.

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Title: A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.
Authors: Tang X; Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan., Machimura T; Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan., Li J; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu, 210023, China., Liu W; School of Geography, South China Normal University, Guangzhou, 510631, China., Hong H; Department of Geography and Regional Research, University of Vienna, Vienna, 1010, Austria. Electronic address: hong_haoyuan@outlook.com.
Source: Journal of environmental management [J Environ Manage] 2020 Oct 01; Vol. 271, pp. 111014. Date of Electronic Publication: 2020 Jul 02.
Publication Type: Journal Article
Journal Info: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.
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  Data: <searchLink fieldCode="AU" term="%22Tang+X%22">Tang X</searchLink>; Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.<br /><searchLink fieldCode="AU" term="%22Machimura+T%22">Machimura T</searchLink>; Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.<br /><searchLink fieldCode="AU" term="%22Li+J%22">Li J</searchLink>; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu, 210023, China.<br /><searchLink fieldCode="AU" term="%22Liu+W%22">Liu W</searchLink>; School of Geography, South China Normal University, Guangzhou, 510631, China.<br /><searchLink fieldCode="AU" term="%22Hong+H%22">Hong H</searchLink>; Department of Geography and Regional Research, University of Vienna, Vienna, 1010, Austria. Electronic address: hong_haoyuan@outlook.com.
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  Data: <searchLink fieldCode="JN" term="%220401664%22">Journal of environmental management</searchLink> [J Environ Manage] 2020 Oct 01; Vol. 271, pp. 111014. <i>Date of Electronic Publication: </i>2020 Jul 02.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Academic+Press%22">Academic Press </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>0401664 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1095-8630 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2203014797%22">03014797 </searchLink><i>NLM ISO Abbreviation: </i>J Environ Manage <i>Subsets: </i>MEDLINE
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      – Type: doi
        Value: 10.1016/j.jenvman.2020.111014
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      – Code: eng
        Text: English
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      – TitleFull: A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment.
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            NameFull: Machimura T
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              Text: 2020 Oct 01
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