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Random Forests

Random Shapley Forests: Cooperative Game Based Random Forests with Consistency

Random Shapley Forests: Cooperative Game Based Random Forests with Consistency

... of random forests. For example, an online random forests classification algorithm was proposed by Denil et ...the random survival forests was proposed by Ishwaran et ...the ...

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SIRUS: Making Random Forests Interpretable

SIRUS: Making Random Forests Interpretable

... as random forests or neural networks, are often qualied as black-boxes because of the high number and complexity of operations involved in their prediction ...on random forests, which takes ...

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Conditional Variable Importance for Random Forests

Conditional Variable Importance for Random Forests

... the random forests are built, are built recursively in that the next splitting varia- ble is selected by means of locally optimizing a criterion (such as the Gini gain in the traditional CART algorithm ...

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A Fine-Grained Random Forests using Class Decomposition

A Fine-Grained Random Forests using Class Decomposition

... and random trees that make up Random Forests, Gain ratio, MDL (Minimum Description Length), Myopic ReliefF and ReliefF were ...unlike Random Forest in its traditional form, weighted voting was ...

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Variable selection using Random Forests

Variable selection using Random Forests

... in random forests for vari- able ...on random forests and to use it to propose a two-steps algorithm for two classical problems of variable selection starting from variable importance ...the ...

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Consistency of Random Forests and Other Averaging Classifiers

Consistency of Random Forests and Other Averaging Classifiers

... It is not so clear what happens in this example if the successive cuts are made by minimizing the empirical error. Whether the middle square is ever cut will depend on the precise form of the stopping rule and the exact ...

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Spam Detection by Random Forests Algorithm

Spam Detection by Random Forests Algorithm

... learning. Random forest (or random forests) is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual ...the ...

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Are Random Forests Truly the Best Classifiers?

Are Random Forests Truly the Best Classifiers?

... “The random forest is clearly the best family of classifiers” is ...why random forests are the best family: “The eight random forest classifiers are included among the 25 best classifiers ...

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Random Prism: a noise-tolerant alternative to Random Forests

Random Prism: a noise-tolerant alternative to Random Forests

... known Random Forests approach has been reviewed. Random Prism is inspired by the Prism family of algorithms, the Random Decision Forests and the Random Forests ...

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Improving random forests by feature dependence analysis

Improving random forests by feature dependence analysis

... LFS may have potential application on time-series data. Here time-series refers to data with each feature a record at a particular time point, not the response variable being time-series. The features of this type of ...

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Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife

... a random forest is usually not a good idea, as it requires forming a large number of base ...small random forests with around B = 10 trees and then applying a bias correction to remove the extra ...

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Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers

... where random forests and AdaBoost yield the strongest performance with respect to the Bayes ...for random forests via the averaging over decision trees, it is less obvious for ...

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Classification of PolSAR Images by Stacked Random Forests

Classification of PolSAR Images by Stacked Random Forests

... Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar ...several Random Forest instances in a sequence where each individual uses the class estimate of its ...

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One Class Splitting Criteria for Random Forests

One Class Splitting Criteria for Random Forests

... Random Forests (RFs) are strong machine learning tools Breiman (2001), comparing well with state-of-the-art methods such as SVM or boosting algorithms Freund et ...different random sub-samples of the ...

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Random Forests for Evaluating Pedagogy and Informing Personalized Learning

Random Forests for Evaluating Pedagogy and Informing Personalized Learning

... While each of these studies mentions directing the students identified as at-risk to tutoring (Zhang et al., 2010), generic interventions (Delen, 2010; Macfayden and Dawson, 2010), or ad- ditional help or attention ...

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ABC random forests for Bayesian parameter inference

ABC random forests for Bayesian parameter inference

... For the observed dataset used in this study, posterior expectations and quantiles of the parameters of interest ra and N2/Na are reported in Tables S6 and S7. Expectation and CI values substantially vary for both ...

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Identifying Twitter Spam by Utilizing Random Forests

Identifying Twitter Spam by Utilizing Random Forests

... Before we begin to discuss the intricacies of a random forest, we will first consider a single tree. Random forests are constructed from a combination of decision trees. A decision tree is a method ...

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Combination of Random Forests and Neural Networks in Social Lending

Combination of Random Forests and Neural Networks in Social Lending

... Social lending, also known as peer-to-peer lending, provides customers with a platform to borrow and lend money online. It is now rapidly gaining its pop- ularity for its superior monetary advantage comparing to banks ...

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Privately  Evaluating  Decision  Trees   and  Random  Forests

Privately Evaluating Decision Trees and Random Forests

... In this work, we focus on one commonly used class of classifiers: decision trees and random forests [43, 19]. Decision trees are simple classifiers that consist of a collection of decision nodes arranged in ...

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Cluster ensemble based on Random Forests for genetic data

Cluster ensemble based on Random Forests for genetic data

... Background: Clustering plays a crucial role in several application domains, such as bioinformatics. In bioinformatics, clustering has been extensively used as an approach for detecting interesting patterns in genetic ...

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