Application Research of Ensemble Learning Frameworks

Wang, Kunkun; Liu, Xianda; Zhao, Jianming; Gao, Hongwei; Zhang, Zhen

doi:10.1109/cac51589.2020.9326882

Cited by 15 publications

(4 citation statements)

References 34 publications

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“…Ensemble learning [61] • The aim is to yield an optimal model by learning from a combination of numerous kinds of models.…”

Section: Methodsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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“…Ensemble learning [61] • The aim is to yield an optimal model by learning from a combination of numerous kinds of models.…”

Section: Methodsmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Ensemble learning, especially the stacking algorithm framework, is better for multiclassification problems and has higher accuracy. 30 Ensemble learning can use multiple weak learning models to build a strong learning model. In order to determine which algorithm is most suitable for our data classification, it needs to be verified one by one, and the traditional method is very timeconsuming and laborious.…”

Section: ■ Experiments and Algorithmsmentioning

confidence: 99%

“…VOCs classification is a multiclassification problem. Ensemble learning, especially the stacking algorithm framework, is better for multiclassification problems and has higher accuracy . Ensemble learning can use multiple weak learning models to build a strong learning model.…”

Section: Experiments and Algorithmsmentioning

confidence: 99%

Selective Sensing of Volatile Organic Compounds Using an Electrostatically Formed Nanowire Sensor Based on Automatic Machine Learning

Yang

Mukherjee

et al. 2023

ACS Sens.

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With the development of Internet of Things technology, various sensors are under intense development. Electrostatically formed nanowire (EFN) gas sensors are multigate Si sensors based on CMOS technology and have the unique advantages of ultralow power consumption and very large-scale integration (VLSI) compatibility for mass production. In order to achieve selectivity, machine learning is required to accurately identify the detected gas. In this work, we introduce automatic learning technology, by which the common algorithms are sorted and applied to the EFN gas sensor. The advantages and disadvantages of the top four tree-based model algorithms are discussed, and the unilateral training models are ensembled to further improve the accuracy of the algorithm. The analyses of two groups of experiments show that the CatBoost algorithm has the highest evaluation index. In addition, the feature importance of the classification is analyzed from the physical meaning of electrostatically formed nanowire dimensions, paving the way for model fusion and mechanism exploration.

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“…RF constructs a large number of decision trees and returns the average prediction for each tree; GBDT is an iterative decision tree algorithm comprising multiple decision trees, with the conclusions of all trees being accumulated into the final result; finally, XGBoost is an improvement on GBDT with highly optimised speeds, supporting regularization to reduce the possibility of over-fitting. Ensemble learning algorithms have become increasingly popular in recent years because they often outperform single ML algorithms [45,46]. However, results vary from application to application and even from dataset to dataset, so it is necessary to test and compare the prediction effectiveness of different ML methods with different subjects and datasets.…”

Section: The Optimal ML Algorithmmentioning

confidence: 99%

Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms

et al. 2023

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There are distinct differences between radiation characteristics of volcanic ash and meteorological clouds, and conventional retrieval methods for cloud base height (CBH) of the latter are difficult to apply to volcanic ash without substantial parameterisation and model correction. Furthermore, existing CBH inversion methods have limitations, including the involvement of many empirical formulae and a dependence on the accuracy of upstream cloud products. A machine learning (ML) method was developed for the retrieval of volcanic ash cloud base height (VBH) to reduce uncertainties in physical CBH retrieval methods. This new methodology takes advantage of polar-orbit active remote-sensing data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), from vertical profile information and from geostationary passive remote-sensing measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Geostationary Radiation Imager (AGRI) aboard the Meteosat Second Generation (MSG) and FengYun-4B (FY-4B) satellites, respectively. The methodology involves a statistics-based algorithm with hybrid use of principal component analysis (PCA) and one of four ML algorithms including the k-nearest neighbour (KNN), extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting decision tree (GBDT) methods. Eruptions of the Eyjafjallajökull volcano (Iceland) during April-May 2010, the Puyehue-Cordón Caulle volcanic complex (Chilean Andes) in June 2011, and the Hunga Tonga-Hunga Ha’apai volcano (Tonga) in January 2022 were selected as typical cases for the construction of the training and validation sample sets. We demonstrate that a combination of PCA and GBDT performs more accurately than other combinations, with a mean absolute error (MAE) of 1.152 km, a root mean square error (RMSE) of 1.529 km, and a Pearson’s correlation coefficient (r) of 0.724. Use of PCA as an additional process before training reduces feature relevance between input predictors and improves algorithm accuracy. Although the ML algorithm performs well under relatively simple single-layer volcanic ash cloud conditions, it tends to overestimate VBH in multi-layer conditions, which is an unresolved problem in meteorological CBH retrieval.

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Application Research of Ensemble Learning Frameworks

Cited by 15 publications

References 34 publications

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Selective Sensing of Volatile Organic Compounds Using an Electrostatically Formed Nanowire Sensor Based on Automatic Machine Learning

Retrieval of Volcanic Ash Cloud Base Height Using Machine Learning Algorithms

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