A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C

DOI10.1007/s11634-020-00418-3zbMath1474.90384arXiv1912.01819OpenAlexW3081527501MaRDI QIDQ2022488

Theodoros Evgeniou, Foster Provost, Yanou Ramon, David Martens

Publication date: 29 April 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.01819

zbMATH Keywords

binary classificationtextual datacomparative studybehavioral datacounterfactual explanationsexplainable artificial intelligenceexplanation algorithmsinstance-level explanations

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Combinatorial optimization (90C27) General topics in artificial intelligence (68T01)

Related Items (2)

Mathematical optimization in classification and regression trees ⋮ On optimal regression trees to detect critical intervals for multivariate functional data

Uses Software

• MovieLens
• shap

Cites Work

• Unnamed Item
• Comprehensible credit scoring models using rule extraction from support vector machines
• Optimal Multi-Way Number Partitioning
• Random forests

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