Preference Relations and Families of Probabilities: Different Sides of the Same Coin

Couso, Inés

doi:10.1007/978-3-319-08795-5_1

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Reflected That Misclassifications Of Elements Class 1 Have M1

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Cited by 3 publications

(2 citation statements)

References 29 publications

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“…According to Couso [2] and Couso and Dubois [3] a general formulation encompasses a wide variety of stochastic orderings from what different orderings between models can be derived:…”

Section: Examplementioning

confidence: 99%

Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach

Couso

Sánchez

2016

Information Sciences

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“…According to Couso [2] and Couso and Dubois [3] a general formulation encompasses a wide variety of stochastic orderings from what different orderings between models can be derived:…”

Section: Examplementioning

confidence: 99%

Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach

Couso

Sánchez

2016

Information Sciences

Self Cite

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“…According to [1,2] a general formulation encompasses a wide variety of stochastic orderings from what different orderings between models can be derived: Definition 4 Let (Ω, A, P ) be a probability space. A random variable Z : Ω → R is preferred to another one Z : Ω → R if and only if…”

Section: Reflected That Misclassifications Of Elements Class 1 Have Mmentioning

confidence: 99%

Generalized stochastic orderings applied to the study of performance of machine learning algorithms for low quality data

Couso¹,

Sánchez²

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Usually, the expected loss minimization criterion is used in order to look for the optimal model that expresses a certain response variable as a function of a collection of attributes. We generalize this criterion, in order to be able to deal also with those situations where a numerical loss function makes no sense or is not provided by the expert. In a first stage, we consider the new framework in standard situations, where both the collection of attributes and the response variables are observed with precision. In a second one, we assume that we are just provided with imprecise information about them (in terms of set-valued data sets). We cast some comparison criteria from the recent literature on learning methods from low-quality data as particular cases of our general approach.

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Modeling a Decision-Maker's Choice Behavior Through Perceived Values

Aggarwal

2019

IEEE Trans. Syst. Man Cybern, Syst.

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Preference Relations and Families of Probabilities: Different Sides of the Same Coin

Cited by 3 publications

References 29 publications

Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach

Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach

Generalized stochastic orderings applied to the study of performance of machine learning algorithms for low quality data

Modeling a Decision-Maker's Choice Behavior Through Perceived Values

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