Abstract
Traditional classification tasks deal with assigning instances to a single label. However, some real world databases classes are structured in a hierarchy and its instances can have their classes associated with two or more paths in the hierarchical structure. In this case, such situations are referred as hierarchical multi-label classification problems. The purpose of this paper is to explore the concept of hierarchical multi-label classification problems and present a solution based on Learning Classifier Systems (LCS) to solve this kind of problem. The Hierarchical Learning Classifier System Multi-label (HLCS-Multi) proposed, presents a comprehensive solution to hierarchical multi-label classification problems building a global classifier to predict all classes in the application domain.
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Romão, L.M., Nievola, J.C. (2015). Hierarchical Multi-label Classification Problems: An LCS Approach. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 12th International Conference. Advances in Intelligent Systems and Computing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-319-19638-1_11
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DOI: https://doi.org/10.1007/978-3-319-19638-1_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19637-4
Online ISBN: 978-3-319-19638-1
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