Factual and counterfactual explanations in fuzzy classification trees
Classification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to...
| Autores: | , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/44493 |
| Acceso en línea: | http://dx.doi.org/10.1109/tfuzz.2022.3179582 https://hdl.handle.net/10578/44493 |
| Access Level: | acceso abierto |
| Palabra clave: | Counterfactual explanations Explainable artificial intelligence (XAI) Factual explanations Fuzzy decision trees Fuzzy reasoning Robustness |
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Factual and counterfactual explanations in fuzzy classification treesFernández Martín, Guillermo TomásAledo Sánchez, Juan ÁngelGámez Martín, José AntonioPuerta Callejón, José MiguelCounterfactual explanationsExplainable artificial intelligence (XAI)Factual explanationsFuzzy decision treesFuzzy reasoningRobustnessClassification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to learn models in an increasingly accurate way. However, their main disadvantage compared to other simpler algorithms, e.g., decision trees, is the loss of interpretability for both the model and the individual classifications, which may become a major drawback because of the increasing number of applications where it is advisable and even compulsory to provide an explanation. A well-accepted practice is to build an explainable model that can mimic the behavior of the (more complex) classifier in the neighborhood of the instance to be explained. Nonetheless, the generation of explanations in such white box models is not trivial either, which has generated intense research. It is common to generate two types of explanations, factual explanations and counterfactual explanations, which complement each other to justify why the instance has been classified into a certain class or category. In this work, we propose the definition of factual and counterfactual explanations in the frame of fuzzy decision trees, where multiple branches can be fired at once. Our proposal is centered around the definition of factual explanations that can contain more than a single rule, in contrast to the current standard that is limited to considering a single rule as a factual explanation. Moreover, we introduce the idea of robust factual explanation. Finally, we provide procedures to obtain counterfactual explanations from the instance and also from a factual explanation.IEEE Press202520252022info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://dx.doi.org/10.1109/tfuzz.2022.3179582https://hdl.handle.net/10578/44493reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésPID2019–106758GB–C33AEI/ 10.13039/501100011033SBPLY/17/180501/000493FPU19/02930info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/444932026-05-27T07:36:41Z |
| dc.title.none.fl_str_mv |
Factual and counterfactual explanations in fuzzy classification trees |
| title |
Factual and counterfactual explanations in fuzzy classification trees |
| spellingShingle |
Factual and counterfactual explanations in fuzzy classification trees Fernández Martín, Guillermo Tomás Counterfactual explanations Explainable artificial intelligence (XAI) Factual explanations Fuzzy decision trees Fuzzy reasoning Robustness |
| title_short |
Factual and counterfactual explanations in fuzzy classification trees |
| title_full |
Factual and counterfactual explanations in fuzzy classification trees |
| title_fullStr |
Factual and counterfactual explanations in fuzzy classification trees |
| title_full_unstemmed |
Factual and counterfactual explanations in fuzzy classification trees |
| title_sort |
Factual and counterfactual explanations in fuzzy classification trees |
| dc.creator.none.fl_str_mv |
Fernández Martín, Guillermo Tomás Aledo Sánchez, Juan Ángel Gámez Martín, José Antonio Puerta Callejón, José Miguel |
| author |
Fernández Martín, Guillermo Tomás |
| author_facet |
Fernández Martín, Guillermo Tomás Aledo Sánchez, Juan Ángel Gámez Martín, José Antonio Puerta Callejón, José Miguel |
| author_role |
author |
| author2 |
Aledo Sánchez, Juan Ángel Gámez Martín, José Antonio Puerta Callejón, José Miguel |
| author2_role |
author author author |
| dc.subject.none.fl_str_mv |
Counterfactual explanations Explainable artificial intelligence (XAI) Factual explanations Fuzzy decision trees Fuzzy reasoning Robustness |
| topic |
Counterfactual explanations Explainable artificial intelligence (XAI) Factual explanations Fuzzy decision trees Fuzzy reasoning Robustness |
| description |
Classification algorithms have recently acquired great popularity due to their efficiency to generate models capable of solving high complexity problems. Specifically, black box models are the ones that offer the best results, since they greatly benefit from the enormous amount of data available to learn models in an increasingly accurate way. However, their main disadvantage compared to other simpler algorithms, e.g., decision trees, is the loss of interpretability for both the model and the individual classifications, which may become a major drawback because of the increasing number of applications where it is advisable and even compulsory to provide an explanation. A well-accepted practice is to build an explainable model that can mimic the behavior of the (more complex) classifier in the neighborhood of the instance to be explained. Nonetheless, the generation of explanations in such white box models is not trivial either, which has generated intense research. It is common to generate two types of explanations, factual explanations and counterfactual explanations, which complement each other to justify why the instance has been classified into a certain class or category. In this work, we propose the definition of factual and counterfactual explanations in the frame of fuzzy decision trees, where multiple branches can be fired at once. Our proposal is centered around the definition of factual explanations that can contain more than a single rule, in contrast to the current standard that is limited to considering a single rule as a factual explanation. Moreover, we introduce the idea of robust factual explanation. Finally, we provide procedures to obtain counterfactual explanations from the instance and also from a factual explanation. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2025 2025 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
http://dx.doi.org/10.1109/tfuzz.2022.3179582 https://hdl.handle.net/10578/44493 |
| url |
http://dx.doi.org/10.1109/tfuzz.2022.3179582 https://hdl.handle.net/10578/44493 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
PID2019–106758GB–C33 AEI/ 10.13039/501100011033 SBPLY/17/180501/000493 FPU19/02930 |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf application/pdf |
| dc.publisher.none.fl_str_mv |
IEEE Press |
| publisher.none.fl_str_mv |
IEEE Press |
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reponame:RUIdeRA. Repositorio Institucional de la UCLM instname:Consejo Superior de Investigaciones Científicas (CSIC) |
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Consejo Superior de Investigaciones Científicas (CSIC) |
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RUIdeRA. Repositorio Institucional de la UCLM |
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RUIdeRA. Repositorio Institucional de la UCLM |
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15,81155 |