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Random forests classifier training and evaluation

eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines

eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines

... hybrid classifier were 0.6357, 0.6165 and 0.6344, higher than all the base classifiers, verifying that hybrid classi- fiers were performing better than single classifiers. The previous methods used EP300 datasets ...

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Random prism: an alternative to random forests

Random prism: an alternative to random forests

... the TC in the current subset of the training data. The stopping criterion is fulfilled as soon as there are no training instances left that are associated with the TC. Cendrowska’s original Prism algorithm ...

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Random Shapley Forests: Cooperative Game Based Random Forests with Consistency

Random Shapley Forests: Cooperative Game Based Random Forests with Consistency

... Shapley Forests: Cooperative Game Based Random Forests with Consistency Jianyuan Sun, Hui Yu, Guoqiang Zhong, Junyu Dong, Shu Zhang, Hongchuan Yu Abstract—The original random forests ...

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Consistency of random forests

Consistency of random forests

... theoretical random forest as before, but with consecutive cuts performed by optimizing L ⋆ ( · , · ) instead of L n ( · , · ...theoretical random forest; instead of stopping when a cell has a single ...

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Banzhaf random forests: Cooperative game theory based random forests with consistency.

Banzhaf random forests: Cooperative game theory based random forests with consistency.

... Fig. 3. Classification accuracy for different random forests algorithms on several data sets. In these charts, the y-axis shows the classification accuracy and the x-axis indicates different algorithms. ...

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Dynamic Integration with Random Forests

Dynamic Integration with Random Forests

... In Random Forests this is achieved by combining two sources of ...original training set. Second, Random Forests consist of using randomly selected features at each node to grow each ...

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Random Forests for Big Data

Random Forests for Big Data

... crucial. Random subsampling are usually adequate for such tasks, providing the fact that the sampling fraction is large ...situation, random subsampling can be a difficult task (see [14] for a discussion on ...

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Random Forests in Language Modelin

Random Forests in Language Modelin

... only training data for heldout data ...both training and heldout data to get the PPL results on test data and only training data for the heldout-data ...

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The parameter sensitivity of random forests.

The parameter sensitivity of random forests.

... While the results to this point demonstrate both that parameterization powerfully influences prediction accuracy and that the default parameter settings are sub-optimal. However they do not demonstrate if it is possible ...

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Probability Estimation in Random Forests

Probability Estimation in Random Forests

... 10-fold cross validation is mostly commonly used in machine learning. The original data set is randomly separated into 10 subsets with equal sample size N/10. 1 subset is chosen as the testing set and the other 9 are the ...

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Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

... Recently, random forests have become a popular method in computer vision given their capability to handle large training datasets, their high generalization power and speed, and the relative ease of ...

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Random Prism: a noise-tolerant alternative to Random Forests

Random Prism: a noise-tolerant alternative to Random Forests

... to Random Forests Frederic Stahl and Max Bramer Abstract Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and ...

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Training Conditional Random Fields with Multivariate Evaluation Measures

Training Conditional Random Fields with Multivariate Evaluation Measures

... Conditional Random Fields (CRFs) to optimize multivariate evaluation mea- sures, including non-linear measures such as ...any evaluation mea- ...get evaluation measure for these tasks, namely, ...

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A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier

A human activity recognition framework using max-min features and key poses with differential evolution random forests classifier

... 2.2. Random Forests Random forests (RF) were first introduced by Breiman ...attribute evaluation mea- sures and voting weighted, in order to increase strength or de- crease correlation ...

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Chapter 12 Bagging and Random Forests

Chapter 12 Bagging and Random Forests

... aggregated classifier f can be thought of as an approximation to the true average f obtained by replacing the probability distribution p with the bootstrap approximation to p obtained concentrating mass 1/n at ...

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Spam Detection by Random Forests Algorithm

Spam Detection by Random Forests Algorithm

... Finally Section 6 presents conclusion and future work. II. RELATED WORK Ahmed Obied proposed Bayesian Spam Filtering in which he describes a machine learning approach based on Bayesian analysis to filter spam. The filter ...

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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 variable is selected by means of locally optimizing a criterion (such as the Gini gain in the traditional CART algorithm of ...

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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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On pruning and feature engineering in Random Forests.

On pruning and feature engineering in Random Forests.

... unlike training data that was seen by the tree when it was built, OOB data was not seen and therefore, it is a more accurate measure of the tree’s predictive ...

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Regression conformal prediction with random forests

Regression conformal prediction with random forests

... of random forests has been proposed as a strong candidate for regression conformal prediction, since it allows for the necessary calibration to be performed on the out- of-bag examples, thus making it ...

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