Rule-Based Classification for Evidential Data

Belief functions and rough sets: Survey and new insights

“…Remarks and Prospects. While the authors of [96] studied the performance of the discussed feature reduction and classification methods and reported promising empirical results, no comparison with other classifiers for evidential data [5,20,32,40,73] has so far been evaluated in the literature. Furthermore, similarly to the remarks in Section 3.3, also for the case of feature reduction and rule induction it would be interesting to evaluate the behavior and performance of different aggregation as well decision rules.…”

“…, P(m [x] B ) is the set of probability measures compatible with m [x] B as defined in(5), and H(P ) = − p p • log(p) is the Shannon entropy. Thus, for each equivalence class [x] B , a bba m [x] B is obtained and the entropy for this bba is simply computed as the lowest possible entropy for all probability distributions that are compatible with it.…”