# A Hybrid Instance-based Transfer Learning Method

arXiv: https://arxiv.org/abs/1812.01063 · HTML: https://mlsanity.com/paper/1812.01063/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 3 of 3)

- [HUST bearing: a practical dataset for ball bearing fault diagnosis](https://mlsanity.com/paper/2302.12533/) (2023)
- [On the Trustworthiness of Tree Ensemble Explainability Methods](https://mlsanity.com/paper/2110.00086/) (2021)
- [ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings](https://mlsanity.com/paper/2508.13672/) (2025)

## Abstract (arXiv)

In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outperforms a set of baselines including state-of-the-art instance-based transfer learning approaches. Our method uses a probabilistic weighting strategy to fuse information from the source domain to the model learned in the target domain. Our method is generic, applicable to multiple source domains, and robust with respect to negative transfer. We demonstrate the effectiveness of our approach through extensive experiments for two different applications.