Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space

Author:

Yang Wentao,He Huaxi,Wei Dongsheng,Chen Hao

Funder

National Natural Science Foundation of China

Philosophy and Social Science Foundation of Hunan Province

Scientific Research Fund of Hunan Provincial Education Department

Publisher

Springer Science and Business Media LLC

Subject

Economics and Econometrics,Geography, Planning and Development

Reference37 articles.

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4. Breunig MM, Kriegel HP, Ng RT, Sander J (2000). LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on management of data, Dallas, TX, USA, pp 93–104

5. Chen H, Chen L, Albright TP (2007) Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: a case of ragweed (Ambrosia artemisiifolia L.) distribution in China. Chin Sci Bull 52(9):1223–1230

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1. Spatial ensemble learning for predicting the potential geographical distribution of invasive species;International Journal of Geographical Information Science;2024-07-10

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