The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
Using POMDPs for learning cost sensitive decision trees
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“Random forest is an integrated decision tree model that aims to improve prediction performance [98, 99].”
From Machine Unlearning: A Survey (2024) · cites this paper as [99]
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Cites
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Cited by
Sorted by citations
Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164
“Random forest is an integrated decision tree model that aims to improve prediction performance [98, 99].”
BackgroundWhen digital twin meets deep reinforcement learning in multi-UAV path planning
Li, S., Lin, X., Wu, J., Bashir, A. K., & Nawaz, R. (2022) · ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond (DroneCom) · doi:10.1145/3555661.3560865 · Cited by 21
Comparison of machine learning algorithm for Santander dataset
Wijaya, Y. A., Suarna, N., Iin, Hamonangan, R., & Nining, R. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1088/1/012032 · Cited by 18
A post-processing framework for class-imbalanced learning in a transductive setting
Jiang, Z., Lu, Y., Zhao, L., Zhan, Y., & Mao, Q. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123832 · Cited by 15
Zhang, Q., & Liu, Y. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102997 · Cited by 9
Metadata
- Authors
- Maliah, S., & Shani, G.
- Affiliations
- Microsoft (United States) (US)
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Shani, Guy
- ORCID
- Shani, Guy
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 292
- Pages
- 103400
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W3101739879
- Cited by
- 12
- References
- 41
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 1
- FWCI
- 2.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1461 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- Funders
- Israel Science Foundation
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- cost-sensitive classification, decision tree, partially observable Markov decision processes, belief states, feature selection, misclassification cost, expected cost minimization
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- CoChat. (2026). Citation check: Using POMDPs for learning cost sensitive decision trees. https://app.cochat.ai/papers/10.1016/j.artint.2020.103400
- The paper
- Maliah, S., & Shani, G. (2021). Using POMDPs for learning cost sensitive decision trees. Artificial Intelligence. https://doi.org/10.1016/j.artint.2020.103400
