Results 291 to 300 of about 452,858 (310)
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2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2020
This talk will present the ongoing work of developing a Chapel implementation of Random Forest, a popular ensembling learning method utilized both for predictive modeling and feature selection. Language features in Chapel make it possible to easily express shared-memory and distributed-memory implementations of this algorithm.
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This talk will present the ongoing work of developing a Chapel implementation of Random Forest, a popular ensembling learning method utilized both for predictive modeling and feature selection. Language features in Chapel make it possible to easily express shared-memory and distributed-memory implementations of this algorithm.
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Cybernetics and Systems, 2015
Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units.
Leo Guelman +2 more
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Conventional supervised statistical learning models aim to achieve high accuracy in predicting the value of an outcome measure based on a number of input measures. However, in many applications, some type of action is randomized on the observational units.
Leo Guelman +2 more
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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
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Geometry- and Accuracy-Preserving Random Forest Proximities
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Jake S Rhodes +2 more
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Accuracy and diversity-aware multi-objective approach for random forest construction
Expert Systems With Applications, 2023Nour El Islem Karabadji +2 more
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Enriched Random Forest for High Dimensional Genomic Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022Debopriya Ghosh
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A comparison of random forest variable selection methods for classification prediction modeling
Expert Systems With Applications, 2019Jaime Lynn Speiser +2 more
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Random forest in remote sensing: A review of applications and future directions
ISPRS Journal of Photogrammetry and Remote Sensing, 2016Mariana Belgiu, Lucian Drăguţ
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