A scalable decision-tree-based method to explain interactions in dyadic data

• Published in: Decision Support Systems 2019-12, Vol.127, p.113141, Article 113141
• Main Authors: Eiras-Franco, Carlos, Guijarro-Berdiñas, Bertha, Alonso-Betanzos, Amparo, Bahamonde, Antonio
• Format: Article
• Language: English
• Subjects: Datasets; Decision trees; Dyadic data; Explainable artificial intelligence; Interpretable machine learning; Machine learning; Scalable machine learning
• ISSN: 0167-9236; 1873-5797
• DOI: 10.1016/j.dss.2019.113141

Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, the existing methods have poor explainability, a quality that is becoming essential in certain applications. We describe an explainable and scalable method that, operating on dyadic datasets, obtains an easily interpretable high-level summary of the relationship between entities. To do this, we propose a quality measure, which can be configured to a level that suits the user, that factors in the explainability of the model. We report experiments that confirm better results for the proposed method over alternatives, in terms of both explainability and accuracy. We also analyse the method's capacity to extract relevant actionable information and to handle large datasets. •Scalable method that obtains an easily interpretable high-level summary of the relationship between entities on dyadic data.•Approach based on the entropy of value of the learnt utility function.•Increased accuracy and model interpretability with respect to alternatives.•Meaningful and actionable insights retrieved from dyadic data.