Criteria of efficiency for set-valued classification

Vovk, Vladimir; Nouretdinov, Ilia; Fedorova, Valentina; Petej, Ivan; Gammerman, Alexander

doi:10.1007/s10472-017-9540-3

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…Since there are about 400 destination locations and every migrant only selects one as its destination, generating forecast in this case is a typical multi-class classification problem. Following the literature 48 , 49 , we adopt the set-valued forecast method to generate destination forecast from the computed migration probability and evaluating its accuracy. Given the migration probability for every destination location, we first sort all destinations in the descending way by the probability values.…”

Section: Resultsmentioning

confidence: 99%

The education-chasing labor rush in China identified by a heterogeneous migration-network game

Xiao-qi

Zheng

Zhao

et al. 2020

Sci Rep

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Despite persistent efforts in understanding the motives and patterns of human migration behaviors, little is known about the microscopic mechanism that drives migration and its association with migrant types. To fill the gap, we develop a population game model in which migrants are allowed to be heterogeneous and decide interactively on their destination, the resulting migration network emerges naturally as an Nash equilibrium and depends continuously on migrant features. We apply the model to Chinese labor migration data at the current and expected stages, aiming to quantify migration behavior and decision mode for different migrant groups and at different stages. We find the type-specific migration network differs significantly for migrants with different age, income and education level, and also differs from the aggregated network at both stages. However, a deep analysis on model performance suggests a different picture, stability exists for the decision mechanism behind the “as-if” unstable migration behavior, which also explains the relative invariance of low migration efficiency in different settings. Finally, by a classification of cities from the estimated game, we find the richness of education resources is the most critical determinant of city attractiveness for migrants, which gives hint to city managers in migration policy design.

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Section: Resultsmentioning

confidence: 99%

The education-chasing labor rush in China identified by a heterogeneous migration-network game

Xiao-qi

Zheng

Zhao

et al. 2020

Sci Rep

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“…The latter means that for the same input X the set Γ α (X) can be different from one experiment to another depending on the underlying randomness of Γ α . For a broad review of conformal prediction theory with main theoretical and practical advances we refer to Vovk, Gammerman and Shafer (2005a); Vovk et al (2017).…”

Section: Conformal Predictionmentioning

confidence: 99%

Set-valued classification -- overview via a unified framework

Chzhen¹,

Denis²,

Hebiri³

et al. 2021

Preprint

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Multi-class classification problem is among the most popular and well-studied statistical frameworks. Modern multi-class datasets can be extremely ambiguous and single-output predictions fail to deliver satisfactory performance. By allowing predictors to predict a set of label candidates, set-valued classification offers a natural way to deal with this ambiguity. Several formulations of set-valued classification are available in the literature and each of them leads to different prediction strategies. The present survey aims to review popular formulations using a unified statistical framework. The proposed framework encompasses previously considered and leads to new formulations as well as it allows to understand underlying trade-offs of each formulation. We provide infinite sample optimal set-valued classification strategies and review a general plug-in principle to construct data-driven algorithms. The exposition is supported by examples and pointers to both theoretical and practical contributions. Finally, we provide experiments on real-world datasets comparing these approaches in practice and providing general practical guidelines.

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“…Construction of optimal prediction regions were considered in Lei et al (2013) for the case of no covariates, in Lei et al (2018) for the case of symmetric and homoscedastic regression problem, and in Sadinle et al (2019) for the classification problem. Also, see Burnaev and Vovk (2014), and Vovk et al (2017). With no symmetric or homoscedasticity assumptions, the problem of smallest prediction interval for regression was considered in Gupta et al (2021), Sesia and Candès (2020), Izbicki et al (2020), and Kivaranovic et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Finite-sample Efficient Conformal Prediction

Yang,

Kuchibhotla

2021

Preprint

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Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied with any machine learning algorithm that provides point predictions to yield valid prediction regions. Of course, the efficiency (width/volume) of the resulting prediction region depends on the performance of the machine learning algorithm. In this paper, we consider the problem of obtaining the smallest conformal prediction region given a family of machine learning algorithms. We provide two general-purpose selection algorithms and consider coverage as well as width properties of the final prediction region. The first selection method yields the smallest width prediction region among the family of conformal prediction regions for all sample sizes, but only has an approximate coverage guarantee. The second selection method has a finite sample coverage guarantee but only attains close to the smallest width. The approximate optimal width property of the second method is quantified via an oracle inequality. Asymptotic oracle inequalities are also considered when the family of algorithms is given by ridge regression with different penalty parameters.

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Criteria of efficiency for set-valued classification

Cited by 13 publications

References 16 publications

The education-chasing labor rush in China identified by a heterogeneous migration-network game

The education-chasing labor rush in China identified by a heterogeneous migration-network game

Set-valued classification -- overview via a unified framework

Finite-sample Efficient Conformal Prediction

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