A key challenge in decision fusion systems is to determine the best performing combination of local decision makers. This selection process can be performed statically at the training phase or dynamically at the execution phase, taking into consideration various features of the data being processed. Dynamic algorithms for the selection of competent sources are generally more accurate, but they are also computationally more intensive and require more memory. In this research, we propose a fuzzy rule-based approach for dynamic source selection (FDSS) that compresses the knowledge from local sources using a divide-and-conquer strategy along with the basic concepts of coverage and truth value criteria, leading to less memory requirement and faster processing. A top-down approach to FDSS is then used to reach a parameter-free algorithm, i.e. one that avoids the restrictive parameters/threshold settings of FDSS. The rule bases in both approaches are created recursively and use the conditional probabilities of each class’s correctness as the rule’s weight. The proposed approaches are compared against several competing dynamic classifier selection methods based on local accuracy. Results indicate that the proposed fuzzy rule structures are generally faster and require less memory, while they also lead to more accurate decisions from the uncertain decisions from multiple sources.

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