Weighted vote for trees aggregation in Random Forest

Daho, Mostafa El Habib; Settouti, Nesma; Lazouni, Mohammed El Amine; Chikh, Mohammed El Amine

doi:10.1109/icmcs.2014.6911187

Cited by 13 publications

(13 citation statements)

References 18 publications

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“…While the main motivation behind this approach is to create stronger learners out of weak ones, misclassifications can occur at low-level learners (at the first classification rounds) and cause performance degradation of the final algorithm. In this work, we decided to deploy bags of Random Forests (RF) [26], in other words, to combine RF with bagging ensemble, as they both have been reported to deal successfully with variance reduction [15]. Moreover, RF is considered one of the most appropriate algorithms for online traffic classification [5] and bagging ensemble for imbalanced traffic identification [1].…”

Section: Theoretical Foundations a Bagging Ensemblementioning

confidence: 99%

Online Data Center Traffic Classification Based on Inter-Flow Correlations

et al. 2020

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Today, increasing attention is being paid to Data Center (DC) traffic classification since these infrastructures have become the heart of a variety of time-sensitive and data-intensive service platforms. Classification provides the required tools for better understanding traffic patterns in order to ensure high Quality of Service (QoS) performances and solve scalability problems. Unfortunately, existing classification algorithms cannot deal efficiently with two critical challenges in DC traffic: inter-class imbalance and critical time constraints. In this paper, we propose a novel correlation-based algorithm following a cost-sensitive approach combined with a Bagged Random Forest (BRF) ensemble algorithm, to address the inter-class imbalance problem while meeting time requirements in a data center environment. In this strategy, a new method based on Reverse k-Nearest Neighbors (RkNN) is proposed to capture the rebalancing weights expressing inter-flow correlations, in order to perform an online classification approach. We demonstrate the efficiency of the algorithm by comparing its performance to several existing methods from data level, algorithm level, and cost-sensitive strategies on four real-world datasets. The results reveal that the proposed algorithm outperforms most approaches in the different datasets in terms of precision, recall, F1 measure, AUC and Kappa, as opposed to other algorithms that result in either high precision with low recall and low precision and high recall causing congestion or resource over provisioning.

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Section: Theoretical Foundations a Bagging Ensemblementioning

confidence: 99%

Online Data Center Traffic Classification Based on Inter-Flow Correlations

et al. 2020

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“…With this, one can develop weights that are proportional to the error of that model, namely, w i = 1 OOB i . Specifically, El Habib Daho et al 23 proposed a weighted stacking method using the OOB error as a way to measure tree importance. Their research provided some fruitful results of improved prediction accuracy.…”

Section: Weighted Averagesmentioning

confidence: 99%

“…Namely, they were only attempting to answer a specific question. On the other hand, El Habib Daho et al 23 In terms of a general weighted bagged ensemble, Hsu 27 proposed a weight-adjusted bagging scheme to multilayer perceptrons for data sets with missing values. Their method performed well compared to traditional bagged ensembles.…”

Section: Weighted Averagesmentioning

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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“…We have to point out that the idea of weighting in RFs is also not new. Most weighting RF methods use weights of classes to deal with imbalanced datasets, for example, [10]. At the same time, there are a lot of publications devoted to more complex weight assignments to every tree.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the Deep Forest Classifier by a Set of Neural Networks

Utkin

Zhuk²

2020

IJCAI

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A Neural Random Forest (NeuRF) and a Neural Deep Forest (NeuDF) as classification algorithms, which combine an ensemble of decision trees and neural networks, are proposed in the paper. The main idea underlying NeuRF is to combine the class probability distributions produced by decision trees by means of a set of neural networks with shared parameters. The networks are trained in accordance with a loss function which measures the classification error. Every neural network can be viewed as a non-linear function of probabilities of a class. NeuDF is a modification of the Deep Forest or gcForest proposed by Zhou and Feng, using NeuRFs. The numerical experiments illustrate the outperformance of NeuDF and show that the NeuRF is comparable with the random forest. Povzetek: V povzetku sta predstavljena dva izvirna algoritma: nevronski naključni gozdovi in nevronski globoki gozdovi.

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Weighted vote for trees aggregation in Random Forest

Cited by 13 publications

References 18 publications

Online Data Center Traffic Classification Based on Inter-Flow Correlations

Online Data Center Traffic Classification Based on Inter-Flow Correlations

Bagged ensembles with tunable parameters

Improvement of the Deep Forest Classifier by a Set of Neural Networks

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