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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Shrub Ensembles for Online Classification

February 1, 2023

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Authors

Sebastian Buschjäger

TU Dortmund, Germany


Sibylle Hess

TU Eindhoven, the Netherlands


Katharina J. Morik

TU Dortmund, Germany


DOI:

10.1609/aaai.v36i6.20560


Abstract:

Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successful online learning methods are Decision Tree (DT) ensembles. DT ensembles provide excellent performance while adapting to changes in the data, but they are not resource efficient. Incremental tree learners keep adding new nodes to the tree but never remove old ones increasing the memory consumption over time. Gradient-based tree learning, on the other hand, requires the computation of gradients over the entire tree which is costly for even moderately sized trees. In this paper, we propose a novel memory-efficient online classification ensemble called shrub ensembles for resource-constraint systems. Our algorithm trains small to medium-sized decision trees on small windows and uses stochastic proximal gradient descent to learn the ensemble weights of these `shrubs'. We provide a theoretical analysis of our algorithm and include an extensive discussion on the behavior of our approach in the online setting. In a series of 2~959 experiments on 12 different datasets, we compare our method against 8 state-of-the-art methods. Our Shrub Ensembles retain an excellent performance even when only little memory is available. We show that SE offers a better accuracy-memory trade-off in 7 of 12 cases, while having a statistically significant better performance than most other methods. Our implementation is available under https://github.com/sbuschjaeger/se-online .

Topics: AAAI

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HOW TO CITE:

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik Shrub Ensembles for Online Classification Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik Shrub Ensembles for Online Classification AAAI 2022, 6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik (2022). Shrub Ensembles for Online Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. Shrub Ensembles for Online Classification. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. 2022. Shrub Ensembles for Online Classification. "Proceedings of the AAAI Conference on Artificial Intelligence". 6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. (2022) "Shrub Ensembles for Online Classification", Proceedings of the AAAI Conference on Artificial Intelligence, p.6123-6131

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik, "Shrub Ensembles for Online Classification", AAAI, p.6123-6131, 2022.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. "Shrub Ensembles for Online Classification". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. "Shrub Ensembles for Online Classification". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6123-6131.

Sebastian Buschjäger||Sibylle Hess||Katharina J. Morik. Shrub Ensembles for Online Classification. AAAI[Internet]. 2022[cited 2023]; 6123-6131.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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