Instance-Based Multi-Label Classification via Multi-Target Distance Regression
Hämäläinen, J., Nieminen, P., & Kärkkäinen, T. (2021). Instance-Based Multi-Label Classification via Multi-Target Distance Regression. In ESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08 (pp. 653-658). ESANN. https://doi.org/10.14428/esann/2021.ES2021-104
© Authors, 2021
Interest in multi-target regression and multi-label classification techniques and their applications have been increasing lately. Here, we use the distance-based supervised method, minimal learning machine (MLM), as a base model for multi-label classification. We also propose and test a hybridization of unsupervised and supervised techniques, where prototype-based clustering is used to reduce both the training time and the overall model complexity. In computational experiments, competitive or improved quality of the obtained models compared to the state-of-the-art techniques was observed.
Parent publication ISBN978-2-87587-082-7
ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Is part of publicationESANN 2021 : Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Online event (Bruges, Belgium), October 06 - 08
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF; Academy Programme, AoF
Additional information about fundingThe work has been supported by the Academy of Finland from the projects 311877 and 315550.
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