Ensemble Learning for 5G Flying Base Station Path Loss Modelling

Sotiroudis, Sotirios P.; Athanasiadou, G.E.; Tsoulos, George V.; Goudos, Sotirios K.

doi:10.23919/eucap53622.2022.9768903

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…The default choice for perturbation is 0.02 [19,39]. An example of using the SMOGN method in a PL prediction problem can be found in [18].…”

Section: Synthetic Generation Of Tabular Datamentioning

confidence: 99%

“…In [17], the authors use noise as the source of diversity in differential privacy synthetic data generation mechanisms. In [18], Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise (SMOGN) [19] is used in order to synthetically augment an imbalanced Path Loss (PL) dataset. The results showed that the ensemble model that incorporated synthetic data led to better results as opposed to the ensemble model that was trained only with the initial data, due to its enhanced predictive capability at the edges of the prediction interval.…”

Section: Introductionmentioning

confidence: 99%

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Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Sotiroudis

2024

Electronics

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Machine Learning (ML)-based models are steadily gaining popularity. Their performance is determined from the amount and the quality of data used at their inputs, as well as from the competence and proper tuning of the ML algorithm used. However, collecting high-quality real data is time-consuming and expensive. Synthetic Data Generation (SDG) is therefore employed in order to augment the limited real data. Moreover, Ensemble Learning (EL) provides the framework to optimally combine a set of standalone ML algorithms (base learners), capitalizing on their individual strengths. Base learner diversity is essential to build a strong ensemble. The proposed method of Targeted Injection of Synthetic Data (TIoSD) combines the EL and SDG concepts in order to further diversify the base learners’ predictions, thus giving rise to an even stronger ensemble model. We have applied TIoSD in two different Path Loss (PL) datasets, using two well-established SDG methods (namely SMOGN and CTGAN). While the conventional ensemble model reached a Minimum Absolute Error (MAE) value of 3.25 dB, the TIoSD-triggered ensemble provided a MAE value of 3.16 dB. It is therefore concluded that targeted synthetic data injection, due to its diversity-triggering characteristics, enhances the ensemble’s performance. Moreover, the ratio between synthetic and real data has been investigated. The results showed that a proportion of 0.1 is optimal.

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“…The default choice for perturbation is 0.02 [19,39]. An example of using the SMOGN method in a PL prediction problem can be found in [18].…”

Section: Synthetic Generation Of Tabular Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Sotiroudis

2024

Electronics

Self Cite

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“…Undersampling can also lead to removal of useful data and oversampling can result in addition of noise to the data [38]. SMOTER has been used in many applications including synthetic data generation for path loss model development, but SMOTER and SMOGN were designed for applications where there are rare extreme target values posing an imbalance in the data [39][40][41][42]. Likewise, GAN have also been used to generate synthetic data for both tabular and image data and to solve class imbalance problems [43,44].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

2022

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Wireless network parameters such as transmitting power, antenna height, and cell radius are determined based on predicted path loss. The prediction is carried out using empirical or deterministic models. Deterministic models provide accurate predictions but are slow due to their computational complexity, and they require detailed environmental descriptions. While empirical models are less accurate, Machine Learning (ML) models provide fast predictions with accuracies comparable to that of deterministic models. Most Empirical models are versatile as they are valid for various values of frequencies, antenna heights, and sometimes environments, whereas most ML models are not. Therefore, developing a versatile ML model that will surpass empirical model accuracy entails collecting data from various scenarios with different environments and network parameters and using the data to develop the model. Combining datasets of different sizes could lead to lopsidedness in accuracy such that the model accuracy for a particular scenario is low due to data imbalance. This is because model accuracy varies at certain regions of the dataset and such variations are more intense when the dataset is generated from a fusion of datasets of different sizes. A Dynamic Regressor/Ensemble selection technique is proposed to address this problem. In the proposed method, a regressor/ensemble is selected to predict a sample point based on the sample’s proximity to a cluster assigned to the regressor/ensemble. K Means Clustering was used to form the clusters and the regressors considered are K Nearest Neighbor (KNN), Extreme Learning Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). The ensembles are any combinations of two, three or four of the regressors. The sample points belonging to each cluster were selected from a validation set based on the regressor that made prediction with lowest absolute error per individual sample point. Implementation of the proposed technique resulted in accuracy improvements in a scenario described by a few sample points in the training data. Improvements in accuracy were also observed on datasets in other works compared to the accuracy reported in the works. The study also shows that using features extracted from satellite images to describe the environment was more appropriate than using a categorical clutter height value.

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Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives

Sun,

Caputo,

Taylor

2024

Applied Eco Perspectives Pol

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Meta‐analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta‐analysis of studies on consumer preferences for plant‐based and lab‐grown meat alternatives using machine‐learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title‐abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out‐of‐sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out‐of‐sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.

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Ensemble Learning for 5G Flying Base Station Path Loss Modelling

Cited by 5 publications

References 16 publications

Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Cultivating Ensemble Diversity through Targeted Injection of Synthetic Data: Path Loss Prediction Examples

Dynamic Regressor/Ensemble Selection for a Multi-Frequency and Multi-Environment Path Loss Prediction

Using machine‐learning methods in meta‐analyses: An empirical application on consumer acceptance of meat alternatives

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