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Soft Computing

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01.08.2020 | Methodologies and Application

Partial label learning based on label distributions and error-correcting output codes

verfasst von: Guangyi Lin, Kunhong Liu, Beizhan Wang, Xiaoyan Zhang

Erschienen in: Soft Computing | Ausgabe 2/2021

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Abstract

Partial label learning (PLL) is a class of weak supervision learning problems in which each data sample has a candidate set of labels, among which only one label is correct. In this paper, a new PLL algorithm with prior information of the label distribution based on ECOC (PL-PIE) is proposed. PL-PIE utilizes the ECOC framework to decompose the problem into multiple binary problems. Different from the instability of the existing random dichotomy, the proposal exploits the prior information of label distribution to generate positive and negative classes with stable performance. Extensive experimental results demonstrate that the proposed PL-PIE algorithm has highly competitive performance compared to the state-of-the-art PLL algorithms.

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Titel: Partial label learning based on label distributions and error-correcting output codes
verfasst von: Guangyi Lin
Kunhong Liu
Beizhan Wang
Xiaoyan Zhang
Publikationsdatum: 01.08.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 2/2021
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05203-0

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