Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data

Li, Hong Bo; Wang, Wei; Ding, Hong; Dong, Jin

doi:10.1109/icebe.2010.99

Cited by 46 publications

(34 citation statements)

References 11 publications

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“…Moreover, numerous researchers studied weighted trees in the random forest again using the OOB as a measure of tree importance. 24,25,26 Ronao and Cho 25 and Winham et al 26 were able to increase the prediction accuracy of their desired classification problem; however, they did so in an ad-hoc nature. Namely, they were only attempting to answer a specific question.…”

Section: Weighted Averagesmentioning

confidence: 99%

“…Namely, they were only attempting to answer a specific question. On the other hand, El Habib Daho et al and Li et al developed the theory of their weighted schemes but were unable to determine specific factors where their weighted scheme worked best.…”

Section: Introductionmentioning

confidence: 99%

“…and Li et al24 developed the theory of their weighted schemes but were unable to determine specific factors where their weighted scheme worked best.…”

mentioning

confidence: 99%

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Bagged ensembles with tunable parameters

Pham

Ólafsson

2018

Computational Intelligence

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Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each with distinct advantages. While boosting methods are typically very tunable with numerous parameters, to date, the type of flexibility this allows has been missing for general bagging ensembles. In this paper, we propose a new tunable weighted bagged ensemble methodology, resulting in a very flexible method for classification. We explore the impact tunable weighting has on the votes of each learner in an ensemble and compare the results with pure bagging and the best known bagged ensemble method, namely, the random forest.

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Section: Weighted Averagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bagged ensembles with tunable parameters

Pham

Ólafsson

2018

Computational Intelligence

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“…This method uses random feature selection in the tree induction process and finally aggregates the predictions of the ensemble to make the final decision. When a new object is to be classified from an input vector, it passes the sample vector to each of the trees in the forest so that each tree can provide a classification decision, and then results of individual trees are combined to choose the classification [30].…”

Section: State-of-the-art Prediction Strategiesmentioning

confidence: 99%

Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

Gatti

Atum

Schiaffino

et al. 2018

Preprint

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Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4class classification) from single-trial EEG. These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.

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“…Some random forest algorithms assign weights to classes [13]. There are algorithms with weights of decision trees [37,42,54]. However, to the best of our knowledge, the weighting schemes have not been used in RSFs.…”

Section: Introductionmentioning

confidence: 99%

A weighted random survival forest

Utkin

Konstantinov

Chukanov

et al. 2019

Knowledge-Based Systems

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A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted avaraging where the weights are assigned to every tree and can be veiwed as training paremeters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell's C-index. Numerical examples with real data illustrate the outperformance of the proposed model in comparison with the original random survival forest.

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Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data

Cited by 46 publications

References 11 publications

Bagged ensembles with tunable parameters

Bagged ensembles with tunable parameters

Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

A weighted random survival forest

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