Cooperative coevolutionary instance selection for multilabel problems
Autor
García Pedrajas, Nicolás
Cerruela García, Gonzalo
Editor
ElsevierFecha
2021Materia
Multilabel learningInstance selection
Cooperative coevolution
Instance-based learning
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Multilabel classification as a data mining task has recently attracted greater research interest. Many
current data mining applications address problems having instances that belong to more than one
class, which requires the development of new efficient methods. Advantageously using the correlation
among different labels can provide better performance than methods that deal with each label
separately. Instance-based classification models, such as k-nearest neighbors for multilabel datasets,
ML-kNN, are among the best performing methods on any classification task and have also shown
very goog performance in multilabel problems. Despite their simplicity, they achieve comparable
performance to considerably more complex methods. One of the challenges associated with instance-
based classification models is their requirement for storing all the training instances in memory. To
ameliorate this problem, instance selection methods have been proposed. However, their application
to multilabel problems is problematic because the adaptation of most of their concepts to multilabel
problems is difficult. In this paper, we propose a cooperative coevolutionary algorithm for instance
selection for multilabel problems. Two different populations evolve together cooperatively. One of the
populations is devoted to obtaining solutions for each label, whereas the other population combines
these results into solutions for the instance selection for multilabel dataset tasks. On a large set of 70
real-world problems, our approach improves the results of both the ML-kNN method with the whole
dataset and an instance selection method using a standard evolutionary algorithm.