# Transfer Learning-Based Label Proportions Method with Data of Uncertainty

## TL;DR

This paper introduces a transfer learning approach for learning with label proportions that effectively handles uncertain data, improving accuracy and noise robustness over existing methods.

## Contribution

It proposes a novel transfer learning-based framework for LLP with uncertain data, including an objective model and iterative classifier training.

## Key findings

- Achieves higher accuracy than existing LLP methods.
- Demonstrates robustness to noisy data.
- Effective transfer of knowledge between tasks.

## Abstract

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.