Semi-causal decision trees

• Type: Article in International Scientific Journal
• Date: 2022

Authors

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

Journal

Title: Progress in Artificial Intelligence

Vol. 11 No. 1

Pages: 105-119

ISSN: 2192-6352

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 1 Citation

Scopus - 2 Citations

Other information

Authenticus ID: P-00V-JH5

DOI: 10.1007/s13748-021-00262-2

Abstract (EN): Typically, classification algorithms use correlation analysis to make decisions. However, these decisions and the models they learn are not easily understandable for the typical user. Causal discovery is the field that studies the means to find causal relationships in observational data. Although highly interpretable, causal discovery algorithms tend to not perform so well in classification problems. This paper aims to propose a hybrid decision tree approach (SC tree) that mixes causal discovery with correlation analysis through the implementation of a custom metric to split the data in the tree's construction (Semi-causal gain ratio). In the results, the proposed methodology obtained a significant performance improvement (11.26% mean error rate) when compared to several causal baselines CDT-PS (23.67% ) and CDT-SPS (25.14%), matching closely the performance of J48 (10.20%), used as a correlation baseline, in ten binary data sets. Besides, when compared with PC in discrete data sets, the proposed approach obtained substantial improvement (16.17% against 28.07% in terms of mean error rate).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15