  Objective: When training machine learning models, we often assume that the
training data and evaluation data are sampled from the same distribution.
However, this assumption is violated when the model is evaluated on another
unseen but similar database, even if that database contains the same classes.
This problem is caused by domain-shift and can be solved using two approaches:
domain adaptation and domain generalization. Simply, domain adaptation methods
can access data from unseen domains during training; whereas in domain
generalization, the unseen data is not available during training. Hence, domain
generalization concerns models that perform well on inaccessible,
domain-shifted data. Method: Our proposed domain generalization method
represents an unseen domain using a set of known basis domains, afterwhich we
classify the unseen domain using classifier fusion. To demonstrate our system,
we employ a collection of heart sound databases that contain normal and
abnormal sounds (classes). Results: Our proposed classifier fusion method
achieves accuracy gains of up to 16% for four completely unseen domains.
Conclusion: Recognizing the complexity induced by the inherent temporal nature
of biosignal data, the two-stage method proposed in this study is able to
effectively simplify the whole process of domain generalization while
demonstrating good results on unseen domains and the adopted basis domains.
Significance: To our best knowledge, this is the first study that investigates
domain generalization for biosignal data. Our proposed learning strategy can be
used to effectively learn domain-relevant features while being aware of the
class differences in the data.
