Results 11 to 20 of about 3,398,718 (314)
Journal of Insurance Medicine, 2017 For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
Steven J. Rigatti
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IEEE Access, 2022 Random forests of axis-parallel decision trees still show competitive accuracy in various tasks; however, they have drawbacks that limit their applicability. Namely, they perform poorly for multidimensional sparse data. A straightforward solution is to create forests of decision trees with oblique splits; however, most training approaches have low ...
Dmitry Devyatkin, Oleg G. Grigoriev
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Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractsidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID ...
Alejandro Mantero, Hemant Ishwaran
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, 2022 Flow chart of classification using Random Forest algorithm (Source: https://www.section.io/engineering-education/introduction-to-random-forest-in-machine-learning/).
Pham Minh Hai (14201069) +7 more
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Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution [PDF]
, 2006 Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers ...
Zeileis, Achim +11 more
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Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics [PDF]
, 2012 The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with ...
König, Inke R. +3 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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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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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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