*3.2.3. Combination strategies for continuous output classifiers*

For the ensemble decision of continuous output problems, the probabilities for each class over all classifiers can be used for fusion. Lingenfelser et al. [95] refer to this probability as support and we adopt this terminology. Using these probabilities, several decision-level combination rules are conceived. We detail only a subset of these rules. The maximum rule stipulates that the ensemble decision for an observed feature vector corresponds to the class with the largest support. The sum rule sums the total support for each class chosen by any of the classifiers. Then, the class with the largest support is chosen as the ensemble decision. Similarly, the mean rule calculates the mean support for each chosen class as opposed to the sum. Instead of calculating the mean, a weighted average of total support for each chosen class can also be calculated. Finally, the product rule is similar to the sum rule, except for the use of the multiplication operation instead of the addition for the calculation of the total support.
