Pruning Optimum-Path Forest Ensembles Using Quaternion-based Optimization
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Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together. In this paper, we present an ensemble pruning approach of Optimum-Path Forest classifiers based on metaheuristics, as well as we introduced the concept of quaternions in ensemble pruning strategies. Experimental results over synthetic and real datasets showed the effectiveness and efficiency of the proposed approach for classification problems.