Multi-source domain adaptation for regression

• URL: http://arxiv.org/abs/2312.05460v1
• Date: Sat, 9 Dec 2023 04:09:37 GMT
• Title: Multi-source domain adaptation for regression
• Authors: Yujie Wu, Giovanni Parmigiani and Boyu Ren
• Abstract summary: Multi-source domain adaptation (DA) aims at leveraging information from more than one source domain to make predictions in a target domain. We extend a flexible single-source DA algorithm for classification through outcome-coarsening to enable its application to regression problems. We then augment our single-source DA algorithm for regression with ensemble learning to achieve multi-source DA.
• Score: 2.8648412780872845
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-source domain adaptation (DA) aims at leveraging information from more than one source domain to make predictions in a target domain, where different domains may have different data distributions. Most existing methods for multi-source DA focus on classification problems while there is only limited investigation in the regression settings. In this paper, we fill in this gap through a two-step procedure. First, we extend a flexible single-source DA algorithm for classification through outcome-coarsening to enable its application to regression problems. We then augment our single-source DA algorithm for regression with ensemble learning to achieve multi-source DA. We consider three learning paradigms in the ensemble algorithm, which combines linearly the target-adapted learners trained with each source domain: (i) a multi-source stacking algorithm to obtain the ensemble weights; (ii) a similarity-based weighting where the weights reflect the quality of DA of each target-adapted learner; and (iii) a combination of the stacking and similarity weights. We illustrate the performance of our algorithms with simulations and a data application where the goal is to predict High-density lipoprotein (HDL) cholesterol levels using gut microbiome. We observe a consistent improvement in prediction performance of our multi-source DA algorithm over the routinely used methods in all these scenarios.

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