A framework for learning fuzzy rule-based models with epistemic set-valued data and generalized loss functions

Sánchez, Luciano; Couso, Inés

doi:10.1016/j.ijar.2017.10.008

Cited by 4 publications

(1 citation statement)

References 44 publications

(47 reference statements)

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“…The impact of model assumptions in the context of incomplete data is an important aspect of study in literature (cf., e.g. Hüllermeier, ; Sánchez & Couso, ; Schollmeyer & Augustin, ). We study in particular the effect of the regression model assumptions on the estimated coarsening parameters.…”

Section: Introductionmentioning

confidence: 99%

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

Plass

Cattaneo

Augustin

et al. 2019

Int Statistical Rev

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Summary In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.

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Section: Introductionmentioning

confidence: 99%

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

Plass

Cattaneo

Augustin

et al. 2019

Int Statistical Rev

View full text Add to dashboard Cite

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Choosing the Decision Hyper-parameter for Some Cautious Classifiers

Imoussaten¹

2022

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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The study of the hyper-parameter modelling the decision rule of the cautious classifiers based on the Fβ measure

Imoussaten¹

2023

Array

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A framework for learning fuzzy rule-based models with epistemic set-valued data and generalized loss functions

Cited by 4 publications

References 44 publications

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

Choosing the Decision Hyper-parameter for Some Cautious Classifiers

The study of the hyper-parameter modelling the decision rule of the cautious classifiers based on the Fβ measure

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