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Random Structures & Algorithms, 1994
AbstractA random recursive forest is defined as a union of random recursive trees. We find the expected number of trees in the uniform random recursive forest as well as the number of vertices of given degree, the maximum degree, the height of vertices, the order of branches, the root of the component containing a given vertex, and the last root of ...
Krystyna T. Balinska +2 more
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AbstractA random recursive forest is defined as a union of random recursive trees. We find the expected number of trees in the uniform random recursive forest as well as the number of vertices of given degree, the maximum degree, the height of vertices, the order of branches, the root of the component containing a given vertex, and the last root of ...
Krystyna T. Balinska +2 more
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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
openaire +1 more source
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 +2 more sources
Geometry- and Accuracy-Preserving Random Forest Proximities
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Kevin R Moon, Jake S Rhodes
exaly
Accuracy and Diversity-Aware Multi-Objective Approach for Random Forest Construction
Expert Systems With Applications, 2023Sabeur Aridhi +2 more
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Prediction Method of Dissolved Gas in Transformer Oil Based on Firefly Algorithm - Random Forest
Lancet, The, 1966Yunlong Ma, Xiuguang Li, J A Loraine
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A Survey of Random Forest Based Methods for Intrusion Detection Systems
ACM Computing Surveys, 2019Paulo Ângelo Alves Resende
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