| Abstract |
In multi-domain operations, different domains get different modalities of input signals, and as a result end up training different models for the same decision-making task. The input modalities could be overlapping with each other, which leads to the situation that models created in one domain may be reusable partially for tasks being conducted in other domains. In order to share the knowledge embedded in different models trained independently in each individual domain, we propose the concept of hybrid policy-based ensembles, in which the heterogeneous models from different domains are combined into an ensemble whose operations are controlled by policies specifying which subset of the models ought to be used for an operation. We show how these policies can expressed based on properties of training datasets, and discuss the performance of these hybrid policy-based ensembles on a dataset used for training network intrusion detection models. |