  Domain adaptation is an attractive approach given the availability of a large
amount of labeled data with similar properties but different domains. It is
effective in image classification tasks where obtaining sufficient label data
is challenging. We propose a novel method, named SELDA, for stacking ensemble
learning via extending three domain adaptation methods for effectively solving
real-world problems. The major assumption is that when base domain adaptation
models are combined, we can obtain a more accurate and robust model by
exploiting the ability of each of the base models. We extend Maximum Mean
Discrepancy (MMD), Low-rank coding, and Correlation Alignment (CORAL) to
compute the adaptation loss in three base models. Also, we utilize a two-fully
connected layer network as a meta-model to stack the output predictions of
these three well-performing domain adaptation models to obtain high accuracy in
ophthalmic image classification tasks. The experimental results using
Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate
the effectiveness of the proposed model.
