Detalhes
Nome
Rita Paula RibeiroCluster
InformáticaCargo
Investigador SéniorDesde
01 janeiro 2008
Nacionalidade
PortugalCentro
Laboratório de Inteligência Artificial e Apoio à DecisãoContactos
+351220402963
rita.p.ribeiro@inesctec.pt
2022
Autores
Davari, N; Pashami, S; Veloso, B; Fan, YT; Pereira, PM; Ribeiro, RP; Gama, J; Nowaczyk, S;
Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022
Abstract
2022
Autores
Shaji, N; Gama, J; Ribeiro, RP; Gomes, P;
Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022
Abstract
Non-traditional data like the applicant’s bank statement is a significant source for decision-making when granting loans. We find that we can use methods from network science on the applicant’s bank statements to convert inherent cash flow characteristics to predictors for default prediction in a credit scoring or credit risk assessment model. First, the credit cash flow is extracted from a bank statement and later converted into a visibility graph or network. Afterwards, we use this visibility network to find features that predict the borrowers’ repayment behaviour. We see that feature selection methods select all the five extracted features. Finally, SMOTE is used to balance the training data. The model using the features from the network and the standard features together is shown having superior performance compared to the model that uses only the standard features, indicating the network features’ predictive power. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2022
Autores
Soares, N; Goncalves, JF; Vasconcelos, R; Ribeiro, RP;
Publicação
ADVANCES IN INTELLIGENT DATA ANALYSIS XX, IDA 2022
Abstract
Biodiversity loss is a hot topic. We are losing species at a high rate, even before their extinction risk is assessed. The International Union for Conservation of Nature (IUCN) Red List is the most complete assessment of all species conservation status, yet it only covers a small part of the species identified so far. Additionally, many of the existing assessments are outdated, either due to the ever-evolving nature of taxonomy, or to the lack of reassessments. The assessment of the conservation status of a species is a long, mostly manual process that needs to be carefully done by experts. The conservation field would gain by having ways of automating this process, for instance, by prioritising the species where experts and financing should focus on. In this paper, we present a pipeline used to derive a conservation dataset out of openly available data and obtain predictions, through machine learning techniques, on which species are most likely to be threatened. We applied this pipeline to the different groups within the Reptilia class as a model of one of the most under-assessed taxonomic groups. Additionally, we compared the performance of models using datasets that include different sets of predictors describing species ecological requirements and geographical distributions such as IUCN’s area and extent of occurrence. Our results show that most groups benefit from using ecological variables together with IUCN predictors. Random Forest appeared as the best method for most species groups, and feature selection was shown to improve results.
2022
Autores
Gama, J; Ribeiro, RP; Veloso, B;
Publicação
IEEE INTELLIGENT SYSTEMS
Abstract
2022
Autores
Veloso, B; Ribeiro, RP; Gama, J; Pereira, PM;
Publicação
SCIENTIFIC DATA
Abstract
Teses supervisionadas
2021
Autor
Rogério Rui Dias da Rocha
Instituição
UP-FEUP
2020
Autor
Tânia Isabel Alexandre Mestre Ferreira
Instituição
UP-FCUP
2019
Autor
Ricardo Dias Azevedo
Instituição
UP-FCUP
2019
Autor
Joel Agostinho Nunes Pinto de Sousa
Instituição
UP-FCUP
2019
Autor
Tânia Margarida Marques Carvalho
Instituição
UP-FCUP
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