Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

Stoimchev, Marjan; Levatić, Jurica; Kocev, Dragi; Džeroski, Sašo

doi:10.1109/tgrs.2024.3426981

IEEE Trans. Geosci. Remote Sensing

2024

DOI: 10.1109/tgrs.2024.3426981

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Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

Marjan Stoimchev,

Jurica Levatić,

Dragi Kocev

et al.

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Application of Label Correlation in Multi-Label Classification: A Survey

Huang,

Hu,

et al. 2024

Applied Sciences

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Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-label classification, each instance in Multi-Label Classification corresponds to multiple labels, and there is a correlation between these labels, which contains a wealth of information. Therefore, the ability to effectively mine and utilize the complex correlations between labels has become a key factor in Multi-Label Classification methods. In recent years, research on label correlations has shown a significant growth trend internationally, reflecting its importance. Given that, this paper presents a survey on the label correlations in Multi-Label Classification to provide valuable references and insights for future researchers. The paper introduces multi-label datasets across various fields, elucidates and categorizes the concept of label correlations, emphasizes their utilization in Multi-Label Classification and associated subproblems, and provides a prospect for future work on label correlations.

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Application of Label Correlation in Multi-Label Classification: A Survey

Huang,

Hu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles

Cited by 1 publication

References 55 publications

Application of Label Correlation in Multi-Label Classification: A Survey

Application of Label Correlation in Multi-Label Classification: A Survey

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