Transfer Learning-Based Label Proportions Method with Data of Uncertainty
This work addresses label proportion learning with uncertain data, offering an incremental improvement for practical applications.
The paper tackles the problem of learning with label proportions (LLP) by proposing a transfer learning-based approach (TL-LLP) that handles uncertain data, achieving better accuracies and reduced noise sensitivity compared to existing methods.
Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice. However, most of the existing LLP methods don't consider the knowledge transfer for uncertain data. This paper presents a transfer learning-based approach for the problem of learning with label proportions(TL-LLP) to transfer knowledge from source task to target task where both the source and target tasks contain uncertain data. Our approach first formulates objective model for the uncertain data and deals with transfer learning at the same time, and then proposes an iterative framework to build an accurate classifier for the target task. Extensive experiments have shown that the proposed TL-LLP method can obtain the better accuracies and is less sensitive to noise compared with the existing LLP methods.