@Article{LorenaJaSiGiLoCaYa:2011:CoMaLe,
               author = "Lorena, Ana C and Jacintho, Luis F. O and Siqueira, Marinez F. and 
                         De Giovanni, Renato and Lohmann, Lucia G. and Carvalho, Andre C. 
                         P. L. F. de and Yamamoto, Missae",
          affiliation = "CMCC Univ Fed ABC, Santo Andre, SP, Brazil and CMCC Univ Fed ABC, 
                         Santo Andre, SP, Brazil and CRIA, Campinas, SP, Brazil and CRIA, 
                         Campinas, SP, Brazil and Univ Sao Paulo, Inst Biociencias, Sao 
                         Paulo, Brazil and Univ Sao Paulo, ICMC, Sao Carlos, SP, Brazil and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Comparing machine learning classifiers in potential distribution 
                         modelling",
              journal = "Expert Systems with Applications",
                 year = "2011",
               volume = "38",
               number = "5",
                pages = "5268--5275",
                month = "may",
             keywords = "Ecological niche modelling, Potential distribution modelling, 
                         Machine learning. SPECIES DISTRIBUTIONS, CLIMATE-CHANGE, HABITAT 
                         SUITABILITY, PREDICTION, BIODIVERSITY, AREAS, INVASIONS, ENVELOPE, 
                         NICHES, SCALE.",
             abstract = "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.",
                  doi = "10.1016/j.eswa.2010.10.031",
                  url = "http://dx.doi.org/10.1016/j.eswa.2010.10.031",
                 issn = "0957-4174",
             language = "en",
        urlaccessdate = "12 maio 2024"
}