Abstract
Machine learning models often need to be adapted to new contexts, for instance, to deal with situations where the target concept changes. In hierarchical classification, the modularity and flexibility of learning techniques allows us to deal directly with changes in the learning problem by readapting the structure of the model, instead of having to retrain the model from the scratch. In this work, we propose a method for adapting hierarchical models to changes in the target classes. We experimentally evaluate our method over different datasets. The results show that our novel approach improves the original model, and compared to the retraining approach, it performs quite competitive while it implies a significantly smaller computational cost.
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Acknowledgements
This work was partially supported by the EU (FEDER) and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, and by Generalitat Valenciana PROMETEOII2015/013. This work has been supported by the Secretary of Higher Education, Science and Technology (SENESCYT: Secretaría Nacional de Educación Superior, Ciencia y Tecnología), of the Republic of Ecuador.
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Silva-Palacios, D., Ferri, C., Ramirez-Quintana, M.J. (2018). Adapting Hierarchical Multiclass Classification to Changes in the Target Concept. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_12
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DOI: https://doi.org/10.1007/978-3-030-00374-6_12
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