40 citations · 137 across the 14 of their papers we have counts for
4 papers · 1 filter
Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review
Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo +2
In this paper we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities, namely, symbolic knowledge extraction (SKE) and i…
Evaluating Machine Learning Models against Clinical Protocols for Enhanced Interpretability and Continuity of Care
Christel Sirocchi, Muhammad Suffian, Federico Sabbatini +2
In clinical practice, decision-making relies heavily on established protocols, often formalised as rules. Concurrently, Machine Learning (ML) models, trained on clinical data, aspi…
Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper
Federico Sabbatini, Roberta Calegari
As opaque decision systems are being increasingly adopted in almost any application field, issues about their lack of transparency and human readability are a concrete concern for…
Clustering-Based Approaches for Symbolic Knowledge Extraction
Federico Sabbatini, Roberta Calegari
Opaque models belonging to the machine learning world are ever more exploited in the most different application areas. These models, acting as black boxes (BB) from the human persp…