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• 184 creator claudia-damato.
• 184 creator floriana-esposito.
• 184 creator giuseppe-rizzo1.
• 184 creator nicola-fanizzi.
• 184 type InProceedings.
• 184 label "Inductive Classification through Evidence-based Models and their Ensembles".
• 184 sameAs 184.
• 184 abstract "In the context of Semantic Web, one of the most important issue related to the class-membership prediction task (by using inductive models) on ontological knowledge bases concerns with the class-imbalance of the training examples, mostly due to the heterogeneous nature and the incompleteness of the knowledge bases. An ensemble learning approach has been proposed to cope with this problem. However, the majority voting procedure, exploited for deciding the membership, does not consider explicitly the uncertainty and the conflict that may occur among the classifiers of an ensemble model. Moving from this observation, we propose to integrate the Dempster-Shafer (DS) Theory with ensemble learning by exploiting DS pooling operators for combining conflictual information. Specifically, we propose an algorithm for learning Evidential Terminological Random Forest models to be used for the class-membership prediction task. The algorithm extends Terminological Random Forest in the settings of the Dempster-Shafer Theory. An empirical evaluation showed that the resulting models performs better for datasets with a lot of positive and negative examples and have a less conservative behavior than the voting-based forests.".
• 184 hasAuthorList authorList.
• 184 isPartOf proceedings.
• 184 keyword "Dempster-Shafer Theory".
• 184 keyword "class-membership prediction".
• 184 keyword "uncertainty".
• 184 title "Inductive Classification through Evidence-based Models and their Ensembles".