Approximate classification with web ontologies through evidential terminological trees and forests

In the context of the Semantic Web, assigning individuals to their respective classes is a fundamental reasoning service. It has been shown that, when purely deductive reasoning falls short, this problem can be solved as a prediction task to be accomplished through inductive classification models built upon the statistical evidence elicited from ontological knowledge bases. However also these data-driven alternative classification models may turn out to be inadequate when instances are unevenly distributed over the various targeted classes To cope with this issue, a framework based on logic decision trees and ensemble learning is proposed. The new models integrate the Dempster-Shafer theory with learning methods for terminological decision trees and forests . These enhanced classification models allow to explicitly take into account the underlying uncertainty due to the variety of branches to be followed up to classification leaves (in the context of a single tree) and/or to the different trees within the ensemble model (the forest). In this extended paper, we propose revised versions of the algorithms for learning Evidential Terminological Decision Trees and Random Forests considering alternative heuristics and additional evidence combination rules with respect to our former preliminary works. A comprehensive and comparative empirical evaluation proves the effectiveness and stability of the classification models, especially in the form of ensembles