A survey of multi-label classification based on supervised and semi-supervised learning

Han, Meng; Wu, Huarui; Chen, Zhiqiang; Li, Muhang; Zhang, Xilong

doi:10.1007/s13042-022-01658-9

Cited by 39 publications

(6 citation statements)

References 118 publications

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“…Supervised learning 113 is a process that uses a group of samples with known labels and features to adjust the parameters of the classifier in order to meet the target. In other words, according to the existing datasets, the relationship between inputs and outputs and the optimistic training model can be obtained through supervised learning.…”

Section: Ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

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Additive manufacturing (AM) has emerged as a very promising technology for producing complex metallic components with enhanced design flexibility. However, the mechanical properties and fatigue behavior of AM metals differ significantly from conventionally manufactured materials, thereby presenting challenges in accurately predicting their fatigue life. This study provides a comprehensive overview of recent developments and future trends in fatigue life prediction of AM metals, with a particular emphasis on machine learning (ML) modeling techniques. This review recalls recent developments and achievements in fatigue characteristics of AM metals, ML‐based approaches for fatigue life prediction of AM metals, and non‐ML‐based methodologies for the same purpose. In particular, some commonly used regression and classification techniques for fatigue evaluation of AM metals are summarized and elaborated. The study intends to furnish researchers, engineers, and practitioners in the field of AM with a guidance for the accurate and efficient prediction of fatigue life in AM metal components.

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Section: Ml‐based Methods For Fatigue Life Prediction Of Am Metalsmentioning

confidence: 99%

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Zhan,

He,

Tang

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…Given that this review is about exploring the interplay between multi-label classification and lifelong learning, we do not provide an exhaustive review of all multi-label algorithms. Interested readers are referred to [6], [36], [37] and [38] for more comprehensive reviews of multi-label algorithms.…”

Section: Algorithm Adaptationmentioning

confidence: 99%

Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and Applications

Awal Kassim,

Viktor,

Michalowski

2024

IEEE Access

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Lifelong machine learning concerns the development of systems that continuously learn from diverse tasks, incorporating new knowledge without forgetting the knowledge they have previously acquired. Multi-label classification is a supervised learning process in which each instance is assigned multiple nonexclusive labels, with each label denoted as a binary value. One of the main challenges within the lifelong learning paradigm is the stability-plasticity dilemma, which entails balancing a model's adaptability in terms of incorporating new knowledge with its stability in terms of retaining previously acquired knowledge. When faced with multi-label data, the lifelong learning challenge becomes even more pronounced, as it becomes essential to preserve relations between multiple labels across sequential tasks. This scoping review explores the intersection of lifelong learning and multi-label classification, an emerging domain that integrates continual adaptation with intricate multi-label datasets. By analyzing the existing literature, we establish connections, identify gaps in the existing research, and propose new directions for research to improve the efficacy of multi-label lifelong learning algorithms. Our review unearths a growing number of algorithms and underscores the need for specialized evaluation metrics and methodologies for the accurate assessment of their performance. We also highlight the need for strategies that incorporate real-world data from varying contexts into the learning process to fully capture the nuances of real-world environments.

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“…Self-supervised learning is a mix of supervised learning and unsupervised learning to leverage unlabelled data, in which a model is repeatedly trained and updated using both the labelled and the generated pseudo-labels (that is, predicted labels) for the unlabelled data. This can result in better performance than supervised learning alone (Han et al, 2022;.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Ten deep learning techniques to address small data problems with remote sensing

Safonova¹,

Ghazaryan²,

Stiller³

et al. 2023

Preprint

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Researchers and engineers have increasingly used Deep Learning (DL) for a variety of Remote Sensing (RS) tasks. However, data from local observations or via ground truth is often quite limited for training DL models, especially when these models represent key socio-environmental problems, such as the monitoring of extreme, destructive climate events, biodiversity, and sudden changes in ecosystem states. Such cases, also known as small data problems, pose significant methodological challenges. This review summarises these challenges in the RS domain and the possibility of using emerging DL techniques to overcome them. We show that the small data problem is a common challenge across disciplines and scales that results in poor model generalisability and transferability, yet this has not been investigated in a structured way. We first introduce ten emerging DL techniques: transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning; we also include a validation technique known as spatial k-fold cross validation. These techniques have shown promising potential in other scientific disciplines, but have been rarely applied in the RS domain. We also provide guidance on which learning technique to use in various cases, which helps to create a more methodologically robust DL application (and a greater number of them) that can be used to tackle socially important problems with limited data.

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A survey of multi-label classification based on supervised and semi-supervised learning

Cited by 39 publications

References 118 publications

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling

Multi-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, and Applications

Ten deep learning techniques to address small data problems with remote sensing

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