A scalable decision-tree-based method to explain interactions in dyadic data
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A scalable decision-tree-based method to explain interactions in dyadic dataDate
2019-12Citation
Eiras-Franco, C., Guijarro-Berdiñas, B., Alonso-Betanzos, A., & Bahamonde, A. (2019). A scalable decision-tree-based method to explain interactions in dyadic data. Decision Support Systems, 127, 113141. https://doi.org/10.1016/j.dss.2019.113141
Abstract
[Abstract]: 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.
Keywords
Dyadic data
Machine learning
Interpretable machine learning
Explainable artificial intelligence
Scalable machine learning
Machine learning
Interpretable machine learning
Explainable artificial intelligence
Scalable machine learning
Description
This version of the article: Eiras-Franco, C., Guijarro-Berdiñas, B., Alonso-Betanzos, A., & Bahamonde, A. (2019). ‘A scalable decision-tree-based method to explain interactions in dyadic data’ has been accepted for publication in: Decision Support Systems, 127, 113141. The Version of Record is available online at https://doi.org/10.1016/j.dss.2019.113141.
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
ISSN
0167-9236
1873-5797
1873-5797