Improving active learning on imbalanced datasets by features mixing

Yuzhen Chen; Haibo Ye

doi:10.1117/12.2678956

25 May 2023 Improving active learning on imbalanced datasets by features mixing

Yuzhen Chen, Haibo Ye

Author Affiliations +

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 1271218 (2023) https://doi.org/10.1117/12.2678956
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

When resources are limited, active learning seeks to optimize the dataset labeling process by choosing the most valuable unlabeled samples through sampling strategy. The majority of active learning techniques in use today are intended for balanced datasets. Machine learning, however, frequently deals with unbalanced datasets in the actual world. We think that unbalanced datasets make active learning more difficult. Here, we suggest an innovative active learning strategy for unbalanced datasets called the imbalanced feature-mixing K-center method (IFMK). In order to lessen the imbalance in the unlabeled samples, it develops interpolation between the representation of unlabeled samples by learning the more pertinent generic properties of the class. We employ a regularization term to enhance the proximity and diversity of unlabeled and labeled samples during sample querying. Results from two datasets with a wide range of imbalance variables demonstrate the superiority of our strategy over more current active learning techniques. On a dataset with more imbalance, these improvements are more noticeable.

Citation Download Citation

Yuzhen Chen and Haibo Ye "Improving active learning on imbalanced datasets by features mixing", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 1271218 (25 May 2023); https://doi.org/10.1117/12.2678956

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KEYWORDS

Active learning

Data modeling

Education and training

Statistical modeling

Machine learning

Head

Performance modeling

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