**3.2. Decision-level fusion**

Typically, a classifier makes errors in some area of the feature space [97]. Hence, combining the results of multiple classifiers can alleviate this shortcoming. This is especially true when each classifier is operating on a different modality that corresponds to a separate feature space.

Using decision-level fusion, modalities can be independently classified using separate models and the results are joined using a multitude of possible methods. Therefore, this approach is said to employ an ensemble of classifiers. Ensemble members can belong to the same family or different families of statistical classifiers. In fact, static and dynamic classifiers can both be employed in such a multimodal system.
