Property-Based Testing for Parameter Learning of Probabilistic Graphical Models - Machine Learning and Knowledge Extraction Access content directly
Conference Papers Year : 2020

Property-Based Testing for Parameter Learning of Probabilistic Graphical Models

Anna Saranti
  • Function : Author
  • PersonId : 1115834
Behnam Taraghi
  • Function : Author
  • PersonId : 1067017
Martin Ebner
  • Function : Author
  • PersonId : 1067019

Abstract

Code quality is a requirement for successful and sustainable software development. The emergence of Artificial Intelligence and data driven Machine Learning in current applications makes customized solutions for both data as well as code quality a requirement. The diversity and the stochastic nature of Machine Learning algorithms require different test methods, each of which is suitable for a particular method. Conventional unit tests in test-automation environments provide the common, well-studied approach to tackle code quality issues, but Machine Learning applications pose new challenges and have different requirements, mostly as far the numerical computations are concerned. In this research work, a concrete use of property-based testing for quality assurance in the parameter learning algorithm of a probabilistic graphical model is described. The necessity and effectiveness of this method in comparison to unit tests is analyzed with concrete code examples for enhanced retraceability and interpretability, thus highly relevant for what is called explainable AI.
Fichier principal
Vignette du fichier
497121_1_En_28_Chapter.pdf (876.55 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03414744 , version 1 (04-11-2021)

Licence

Attribution

Identifiers

Cite

Anna Saranti, Behnam Taraghi, Martin Ebner, Andreas Holzinger. Property-Based Testing for Parameter Learning of Probabilistic Graphical Models. 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.499-515, ⟨10.1007/978-3-030-57321-8_28⟩. ⟨hal-03414744⟩
32 View
32 Download

Altmetric

Share

Gmail Facebook X LinkedIn More