Generalised Partial Association in Causal Rules Discovery

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Nogueira, AR (Author) Other; Ferreira, C (Author) Other; João Gama (Author) FEP; Alberto A. Pinto (Author) FCUP

Conference proceedings International

Title: Progress in artificial intelligence: 20th EPIA Conference on Artificial Intelligence, EPIA 2021: Proceedings

Pages: 485-497

20th EPIA Conference on Artificial Intelligence, EPIA 2021

Virtual Event, September 7–9, 2021

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Other information

Authenticus ID: P-00V-CMR

DOI: 10.1007/978-3-030-86230-5_38

Abstract (EN): One of the most significant challenges for machine learning nowadays is the discovery of causal relationships from data. This causal discovery is commonly performed using Bayesian like algorithms. However, more recently, more and more causal discovery algorithms have appeared that do not fall into this category. In this paper, we present a new algorithm that explores global causal association rules with Uncertainty Coefficient. Our algorithm, CRPA-UC, is a global structure discovery approach that combines the advantages of association mining with causal discovery and can be applied to binary and non-binary discrete data. This approach was compared to the PC algorithm using several well-known data sets, using several metrics. © 2021, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific