In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction.
These issues are beyond the scope of this article, but notice that these results of phenomenological analysis shape the characterization of the domain of study and the methodology appropriate to the domain.
These definitions and principles form the basis of causal explanations of all the other universal truths within the domain of study.
While such an approach can still be taken using MODAM, we are able to use specific data of the domain of study, which offers the additional benefit of greater fidelity to the system represented.
Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step.
To prevent such issues, the domain of study and the scale of complexity must be clearly defined.
Risks of bias were highest in the domain of study attrition, which was due to very low response rates [ 31] or an unclear response rate [ 19, 25, 34].
When a high risk of bias was assessed for the domain of study participation, it was because important baseline characteristics such as comorbidity or age were missing.
The domain of study ranges from the theoretical aspects (like existence, uniqueness, periodicity, asymptotic behavior, etc).
The discipline of phenomenology is defined by its domain of study, its methods, and its main results.
This observation might indicate that certain functions are best suited to solve the profile similarity problem in our domain of study.
