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Article Dans Une Revue Knowledge-Based Systems Année : 2020

Disagreement based semi-supervised learning approaches with belief functions

Approche d'apprentissage semi-supervisé basée sur les conflits de fonctions de croyance

Deqiang Han
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Résumé

In many machine learning tasks, it is usually difficult to obtain enough labeled samples. Semi-supervised learning that exploits unlabeled samples in addition to labeled ones has attracted a lot of research attentions. Traditional semi-supervised methods may encounter uncertainty problems and information loss when dealing with those samples having ambiguous class belongingness. In this paper, the uncertainties encountered in semi-supervised learning are addressed using the theory of belief functions, and semi-supervised learning methods based on belief functions are proposed. The proposed methods label the unlabeled data with belief modeling. They can effectively use limited supervised information to facilitate the classification process. Experimental results based on benchmark data sets show that the proposed approaches can effectively exploit unlabeled data and perform better compared with prevailing approaches.
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Dates et versions

hal-02873444 , version 1 (18-06-2020)

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Hongshun He, Deqiang Han, Jean Dezert. Disagreement based semi-supervised learning approaches with belief functions. Knowledge-Based Systems, 2020, 193, pp.105426. ⟨10.1016/j.knosys.2019.105426⟩. ⟨hal-02873444⟩
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