STIOCS: Active learning-based semi-supervised training framework for IOC extraction

Tang, Binhui; Li, Xiaohui; Wang, Junfeng; Ge, Wenhan; Yu, Zhongkun; Lin, Tongcan

doi:10.1016/j.compeleceng.2023.108981

Cited by 4 publications

(1 citation statement)

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“…This iterative process aims to progressively enhance the performance of the model by incorporating human expertise into the ML model. Compared to other ML methods, the AL algorithm excels in efficiently reducing data annotation costs [99]. Additionally, in scenarios characterized by class imbalance or label noise within the dataset, the AL algorithm can enhance data quality, enabling the model to focus more effectively on crucial samples for performance improvement, thereby enhancing the generalization capability of the model.…”

Section: Active Learning (Al) Algorithmmentioning

confidence: 99%

Machine Learning Design for High-Entropy Alloys: Models and Algorithms

Liu,

Yang

2024

Metals

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High-entropy alloys (HEAs) have attracted worldwide interest due to their excellent properties and vast compositional space for design. However, obtaining HEAs with low density and high properties through experimental trial-and-error methods results in low efficiency and high costs. Although high-throughput calculation (HTC) improves the design efficiency of HEAs, the accuracy of prediction is limited owing to the indirect correlation between the theoretical calculation values and performances. Recently, machine learning (ML) from real data has attracted increasing attention to assist in material design, which is closely related to performance. This review introduces common and advanced ML models and algorithms which are used in current HEA design. The advantages and limitations of these ML models and algorithms are analyzed and their potential weaknesses and corresponding optimization strategies are discussed as well. This review suggests that the acquisition, utilization, and generation of effective data are the key issues for the development of ML models and algorithms for future HEA design.

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Section: Active Learning (Al) Algorithmmentioning

confidence: 99%