Improving Prediction with Causal Probabilistic Variables

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Nogueira, AR (Author) Other; João Gama (Author) FEP; Ferreira, CA (Author) Other

Conference proceedings International

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

Pages: 379-390

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 3 Citations

Other information

Authenticus ID: 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).

Language: English

Type (Professor's evaluation): Scientific