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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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Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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Crossbreeding in Random Forest
CoRR, 2021 Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems.
Abolfazl Nadi +2 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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CoRR, 2019 Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned.
Ge, S +4 more
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Machine Learning, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sunwoo Han, Hyunjoong Kim, Yung-Seop Lee
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International Journal of Approximate Reasoning, 2010 AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision ...
Piero P. Bonissone +3 more
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Knowledge-Based SystemsThe Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive ...
Shaker, Mohammad Hossein +1 more
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