.png){kind=icon}
Return
Comparing machine learning classifiers in potential distribution modelling
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2010.10.031.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Species' potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species' potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Ecological niche modelling
Potential distribution modelling
Machine learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
View more organizations
Cited Papers
ShareSaveShareSaveShareSavePredicting the landscape-scale distribution of alien plants and their threat to plant diversity.png)
CONSERVATION BIOLOGY
CONSERVATION BIOLOGYIF5.5
PREAI
ShareSaveEffects of melatonin on oxidative stress, and resistance to bacterial, parasitic, and viral infections: A reviewActa Tropica
Acta TropicaIF0
PREAI
ShareSave View more