RT Book,_Section
T1 Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers
A1 Stepin, Ilia
A1 Alonso Moral, José María
A1 Catalá Bolós, Alejandro
A1 Pereira Fariña, Martín
AB Data-driven classification algorithms have proven highly effective in a range of complex tasks. However, their output is sometimes questioned, as the reasoning behind it may remain unclear due to a high number of poorly interpretable parameters used during training. Evidence-based (factual) explanations for single classifications answer the question why a particular class is selected in terms of the given observations. On the contrary, counterfactual explanations pay attention to why the rest of classes are not selected. Accordingly, we hypothesize that providing classifiers with a combination of both factual and counterfactual explanations is likely to make them more trustworthy. In order to investigate how such explanations can be produced, we introduce a new method to generate factual and counterfactual explanations for the output of pretrained decision trees and fuzzy rule-based classifiers. Experimental results show that unification of factual and counterfactual explanations under the paradigm of fuzzy inference systems proves promising for explaining the reasoning of classification algorithms.
PB IEEE
SN 978-1-7281-6932-3
YR 2020
FD 2020
LK https://hdl.handle.net/10347/42577
UL https://hdl.handle.net/10347/42577
LA eng
NO I. Stepin, J. M. Alonso, A. Catala and M. Pereira-Fariña, "Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers," 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/FUZZ48607.2020.9177629.
NO Jose M. Alonso is a Ramon y Cajal Researcher (RYC- 2016-19802). Alejandro Catala is a Juan de la Cierva Researcher (UC2018-037522-I). This research is funded by the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-099646-B-I00, TIN2017-84796-C2- 1-R, TIN2017-90773-REDT, and RED2018-102641-T) and the Galician Ministry of Education, University and Professional Training (grants ED431F 2018/02, ED431C 2018/29, ED431G/08, ED431G2019/04). Some of the previous grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).
DS Minerva
RD 24 abr 2026