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Integration of local and global features explanation with global rules extraction and generation tools
Tipo de publicação: Artigo
Citação:
Journal: Post-proceedings of EXTRAAMAS 2022
Ano: 2022
Mês: June
Resumo: Widely used in a growing number of domains, Deep Learning predictors are achieving remarkable results. However, the lack of trans- parency (i.e., opacity) of their inner mechanisms has raised trust and employability concerns. Nevertheless, several approaches fostering mod- els of interpretability and explainability have been developed in the last decade. This paper combines approaches for local feature explanation (i.e., Contextual Importance and Utility – CIU) and global feature ex- planation (i.e., Explainable Layers) with a rule extraction system, namely ECLAIRE. The proposed pipeline has been tested in four scenarios em- ploying a breast cancer diagnosis dataset. The results show improvements such as the production of more human-interpretable rules and adherence of the produced rules with the original model.
Palavras-chave: Local explainability · Global explainability · Feature rank- ing · rule extraction
Autores Contreras, Victor H.
Schumacher, Michael
Calvaresi, Davide
Adicionado por: []
Total mark: 0
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  • Extraamas_workshop_Davide_Vict...
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