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. |
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| 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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