**3.3. Hybrid fusion**

When a fusion technique combines feature and decision-level fusion, it is referred to as a hybrid-fusion scheme. For instance, we can achieve fusion in two stages. In the first stage, a classifier can perform feature-level fusion. For example, a single classifier can handle features from audio and video signals. In the second stage, decision-level fusion can be used to combine the results of that classifier with another one operating on physiological (e.g., HRV) features.

Ref. [104] proposes a simple hybrid-fusion approach where the result from the feature-level fusion is fed as an additional input to the decision-level fusion stage. Lingenfelser et al. [95] propose two variants of one method called the one versus rest. This approach creates an ensemble composed of classifiers trained on each feature set (i.e., features from a modality). However, these classifiers model a two-class problem. That is, each one of them is specialized in classifying a single class. One last multiclass classifier is added to the ensemble and is trained on the merged feature set (i.e., features from all modalities). For the first variant, during classification, for an observed sample, the support for a class obtained from its two-class classifiers is multiplied with the support of the multiclass classifier to obtain an accumulated support. The class with the highest accumulated support is chosen as the ensemble decision. The second variant is similar, except that it chooses the best two-class classifier for each class and uses it to calculate accumulated support.
