Improving Prediction with Causal Probabilistic Variables

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Nogueira, AR (Autor) Outra; João Gama (Autor) FEP; Ferreira, CA (Autor) Outra

Ata de Conferência Internacional

Título: Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings

Páginas: 379-390

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00S-0T2

DOI: 10.1007/978-3-030-44584-3_30

Abstract (EN): The application of feature engineering in classification problems has been commonly used as a means to increase the classification algorithms performance. There are already many methods for constructing features, based on the combination of attributes but, to the best of our knowledge, none of these methods takes into account a particular characteristic found in many problems: causality. In many observational data sets, causal relationships can be found between the variables, meaning that it is possible to extract those relations from the data and use them to create new features. The main goal of this paper is to propose a framework for the creation of new supposed causal probabilistic features, that encode the inferred causal relationships between the target and the other variables. In this case, an improvement in the performance was achieved when applied to the Random Forest algorithm. © 2020, The Author(s).

Idioma: Inglês

Tipo (Avaliação Docente): Científica