Relational Active Feature Elicitation for DDDAS

Dynamic Data Driven Applications Systems (DDDAS) utilize data augmentation for system performance. To enhance DDDAS systems with domain experts, there is a need for interactive and explainable active feature elicitation in relational domains in which a small subset of data is fully observed while the rest of the data is minimally observed. The goal is to identify the most informative set of entities for whom acquiring the relations would yield a more robust model. Assuming the presence of a human expert who can interactively score the relations, there is a need for an explainable model designed using the Feature Acquisition via Interaction in Relational domains (FAIR) algorithm. FAIR employs a relational tree-based distance metric to identify the most diverse set of relational examples (entities) to obtain more relational feature information for user refinement. The model that is learned iteratively is usable, interpretable, and explainable.