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2016 IEEE 16th International Conference on Data Mining (ICDM), 2016
Ensemble methods are considered among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly.
Amichai Painsky, Saharon Rosset
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Ensemble methods are considered among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the complexity of each learner typically grows with the size of the dataset. This phenomenon results in an increasing demand for storage space, which may be very costly.
Amichai Painsky, Saharon Rosset
openaire +1 more source
Towards convergence rate analysis of random forests for classification
Artificial Intelligence, 2022Zhi-Hua Zhou, Fan Xu
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Random Forests for Spatially Dependent Data
Journal of the American Statistical Association, 2023Arkajyoti Saha +2 more
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Variable selection using random forests
Pattern Recognition Letters, 2010Robin Genuer, Jean-Michel Poggi
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Navigating Random Forests and related advances in algorithmic modeling
Statistics Surveys, 2009David S Siroky
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Banzhaf random forests: Cooperative game theory based random forests with consistency
Neural Networks, 2018Jianyuan Sun +2 more
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Mining data with random forests: A survey and results of new tests
Pattern Recognition, 2011Antanas Verikas
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