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Multiobjective Optimization Techniques for Selecting Important Metrics in the Design of Ensemble Systems

Ensemble systems are classification structures that apply a two‐level decision‐making process, in which the first level produces the outputs of the individual classifiers and the second level produces the output of the combination method (final output). Although ensemble systems have been proven to be efficient for pattern recognition tasks, its efficient design is not an easy task. This article investigates the influence of two diversity measures when used explicitly to guide the design of ensemble systems. These diversity measures were proposed recently, and they proved to be very interesting for the diversity–accuracy dilemma. To perform this investigation, we will use two well‐known optimization techniques, genetic algorithms, and tabu search, in their mono‐objective and multiobjective versions. As objectives of the optimization techniques, we use error rate and two diversity measures as well as all possible combinations of these three objectives. In this article, we aim to analyze which set of objectives can generate more accurate ensembles. In addition, we aim to analyze whether or not the diversity measures (good and bad diversities) have a positive effect in the design of ensemble systems, mainly if they can replace the error rate as an optimization objective without incurring significant losses in the accuracy level of the generated ensembles.