Generally, classification problems catalog instances according to their target variable with out considering the relation among the different labels. However, there are real problems
in which the different values of the class are related to each other. Because of interest in
this type of problem, several solutions have been proposed, such as cost-sensitive classi fiers. Ensembles have proven to be very effective for classification tasks; however, as far
as we know, there are no proposals that use a genetic-based methodology as the meta heuristic to create the models. In this paper, we present OCEAn, an ordinal classification
algorithm based on an ensemble approach, which makes a final prediction according to
a weighted vote system. This weighted voting takes into account weights obtained by a
genetic algorithm that tries to minimize the cost of classification. To test the performance
of this approach, we compared our proposal with ordinal classification algorithms in the
literature and demonstrated that, indeed, our approach improves on previous resultsMinisterio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-126334
