Results 31 to 40 of about 452,858 (310)
Portfolio Selection Using Random Forest Algorithm
International Journal of Computer Engineering and Data Science, 2022 Portfolio selection has long been a main topic in finance. What stocks should one invest in? How much should one allocate to each stock to maximize gain and minimize risk?
Daname KOLANI
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Double Cost Sensitive Random Forest Algorithm
Journal of Harbin University of Science and Technology, 2021 A Double Cost Sensitive Random Forest (DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data.
ZHOU Yan-long, SUN Guang-lu
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Review of Random Survival Forest method
Journal of Biostatistics and Epidemiology, 2020 Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially ...
Majid Rezaei +4 more
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Random Prism: An Alternative to Random Forests [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach.
Stahl, F., Bramer, Max
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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Research on optimization of random forest algorithm based on spark
, 2022 As society has developed, increasing amounts of data have been generated by various industries. The random forest algorithm, as a classification algorithm, is widely used because of its superior performance.
Pang, C, Geng, S, Wang, S, Zhang, Z
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Unsupervised random forest for affinity estimation
Computational Visual Media, 2021 This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster ...
Yunai Yi +5 more
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Three-Branch Random Forest Intrusion Detection Model
Mathematics, 2022 Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection.
Chunying Zhang +4 more
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An AUC-based Permutation Variable Importance Measure for Random Forests [PDF]
, 2012 The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs).
Silke Janitza +5 more
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Sankhya A, 2018 Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural ...
Biau, Gérard +2 more
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