Using Random forest and Gradient boosting trees to improve wave forecast at a specific location

Callens, Aurélien; Morichon, Denis; Abadie, Stéphane; Delpey, Matthias; Liquet, Benoît

doi:10.1016/j.apor.2020.102339

Cited by 76 publications

(19 citation statements)

References 23 publications

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“…Gradient boosting decision tree (GBDT) and its effective implementations such as XGBoost [3] and LightGBM [4] are widely used machine learning both in industry and academic applications [5,11,12]. In distributed GBDT, the training data is located in different machines and should be partitioned according to the sample level.…”

Section: Federated Gradient Boosting Decision Treementioning

confidence: 99%

“…In this context, it is necessary to consider factors such as privacy protection, unbalanced/skewed data distribution, fairness, to form a closed-loop federated learning system (FLS) [2]. On the other hand, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and has shined in many machine learning and data mining competitions [3,4] as well as real-world applications for its salient results on classification, ranking, prediction, etc., (especially for tabular data mining task) [5]. And several works have studied the horizontal federated GBDT system [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees

Gao

Shi

et al. 2022

Security and Communication Networks

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At present, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and has shined in many data mining competitions and real-world applications for its salient results on classification, ranking, prediction, etc. Federated learning which aims to mitigate privacy risks and costs, enables many entities to keep data locally and train a model collaboratively under an orchestration service. However, most of the existing systems often fail to make an excellent trade-off between accuracy and communication. In addition, they overlook an important aspect: fairness such as performance gains from different parties’ datasets. In this paper, we propose a novel federated GBDT scheme based on the blockchain which can achieve constant communication overhead and good model performance and quantify the contribution of each party. Specifically, we replace the tree-based communication scheme with the pure gradient-based scheme and compress the intermediate gradient information to a limit to achieve good model performance and constant communication overhead in skewed datasets. On the other hand, we introduce a novel contribution allocation scheme named split Shapley value, which can quantify the contribution of each party with a limited gradient update and provide a basis for monetary reward. Finally, we combine the quantification mechanism with blockchain organically and implement a closed-loop federated GBDT system FGBDT-Chain in a permissioned blockchain environment and conduct a comprehensive experiment on public datasets. The experimental results show that FGBDT-Chain achieves a good trade-off between accuracy, communication overhead, fairness, and security under large-scale skewed datasets.

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Section: Federated Gradient Boosting Decision Treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees

Gao

Shi

et al. 2022

Security and Communication Networks

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“…The RF is train with three sets of inputs: the electricity generated, the wave energy flux at the nearest gridpoint, and ocean and atmospheric data. In [163], RFs and Gradient boosting trees are used to improve significant wave height forecasting. Hyperparameter values of these ML algorithms are tuned using bayesian optimization.…”

Section: Rf In Marine and Ocean Energymentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

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Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic-

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“…In this paper, we aim to present the novel approach to video segmentation by using the optimization of segmentation parameters based on Grid-based ensemble method [62]. Our approach is the combination of random forest and gradient boosting decision tree [63], [64], [65], [66], [67], [68], [69], [70]. The system framework of our approach will be explained in section 2.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms

Pensiri¹,

Visutsak²

2022

Preprint

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Video segmentation is crucial in a variety of practical applications especially in computer visions. Most of recent works in video segmentation are focusing on Deep learning based video segmentation, there are rooms for improvement in respect of the evolutionary algorithms. This paper aims to propose the novel method to video segmentation by using the optimization of segmentation parameters based on ensemble-based random forest and gradient boosting decision tree. The experimental results show Pareto front of segmentation parameters (hue, brightness, luminance, and saturation). Our optimization model yields accuracy: 85% +/-8.85 % (micro average: 85.00 %), average class precision: 84.88%, and average class recall: 85%. We also show the video segmentation results based on our optimization method and compare our results with Kinect-based video segmentation.

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Using Random forest and Gradient boosting trees to improve wave forecast at a specific location

Cited by 76 publications

References 23 publications

Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees

Towards Fair and Decentralized Federated Learning System for Gradient Boosting Decision Trees

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Optimization of Hue, Brightness, Luminance, and Saturation Parameters for Video Segmentation Based on Evolutionary Algorithms

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