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These belief function methods that are executed throughout the machine learning domain usually leverage a fusion approach of assorted ensemble techniques to better cope with the learner's final decision boundary, lower samples, and ambiguous course difficulties that normal machine learning approach are likely to acquire trouble resolving.[four][nin