OCEAn: Ordinal classification with an ensemble approach
Belén Vega-Márquez, Isabel A. Nepomuceno-Chamorro, Cristina Rubio-Escudero, José C. Riquelme
Abstract
Generally, classification problems catalog instances according to their target variable without 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 classifiers. 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 metaheuristic 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 results.