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- W3190403603 abstract "The success of Artificial Intelligence at solving real-world problems poses the need for interpretable models, especially in human-centered applications. The multi-class scenario is often present in these environments; however, the majority of research on interpretability has focused on binary classification. In this work, a novel method based on hierarchical decompositions to obtain interpretable multi-class models is introduced. The proposal, named Threshold Control for Nested Dichotomies (TC-ND) method, creates a binary-based hierarchical class structure. Then, it discards meta-classes at each dichotomy of the structure according to a certain level of confidence, pursuing a modular and more comprehensible decomposition of the multi-class problem. The approach presents internal parameters that are optimized using the Differential Evolution algorithm. The goodness of our proposal is assessed using a twofold approach: performance is evaluated by comparing against other state-of-the-art multi-class methods; interpretability is supported by practical examples with counterfactual explanations and a discussion of the advantages that the TC-ND method presents regarding its transparency and auditability." @default.
- W3190403603 created "2021-08-16" @default.
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- W3190403603 date "2021-11-01" @default.
- W3190403603 modified "2023-09-29" @default.
- W3190403603 title "Learning interpretable multi-class models by means of hierarchical decomposition: Threshold Control for Nested Dichotomies" @default.
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- W3190403603 doi "https://doi.org/10.1016/j.neucom.2021.07.097" @default.
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