Comparison of Random Forest and Extreme Gradient Boosting Fingerprints to Enhance an indoor Wifi Localization System

Niang, Mariame; Ndong, Massa; Dioum, Ibra; Diop, Idy; Mashaly, Maggie; Ghany, Mohamed A. Abd El

doi:10.1109/miucc52538.2021.9447676

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…This has also been published by Bentéjac et al 45 who compared XGBoost to several gradient-boosting algorithms. The XGBoost Algorithm was also shown to achieve a lower error value in comparison to random forests by Niang et al 46 . XGBoost is a more regularized form of Gradient Boosting.…”

Section: /15mentioning

confidence: 99%

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Hamadani

Ganai

2023

Preprint

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In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Considering all this, this research was undertaken to evaluate machine learning (ML) algorithms as potential tools for evaluating 52-year data for sheep. Data was appropriately prepared was done before analysis. Winsorization was done for outlier removal. Principal component regression (PCA) and feature selection (FS) were done and based on that, three datasets were created viz. PCA, PCA+ FS, and FS bodyweight prediction. Among the 11 ML algorithms that were evaluated, the correlations between true and predicted values for MARS algorithm, Bayesian ridge regression, Ridge regression, Support Vector Machines, Gradient boosting algorithm, Random forests, XgBoost algorithm, Artificial neural networks, Classification and regression trees, Polynomial regression, K nearest neighbours and Genetic Algorithms were 0.993, 0.992, 0.991,0.991,0.991, 0.99, 0.99,0.984,0.984,0.957, 0.949.0.734 respectively for bodyweights. The top five algorithms for the prediction of bodyweights, were MARS, Bayesian ridge regression, Ridge regression, Support Vector Machines and Gradient boosting algorithm. A total of 12 machine learning models were developed for the prediction of bodyweights in sheep in the present study. It may be said that machine learning techniques can perform predictions with reasonable accuracies and can thus be viable alternatives to conventional strategies.

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Section: /15mentioning

confidence: 99%

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Hamadani

Ganai

2023

Preprint

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“…Comparing with regular bagging approaches with no self-pruning, such as the RF model, RF trees are fully grown to classify a possible category, where variance is reduced to achieve mitigated errors in predictions. In contrast, the XGB uses weak learners that are defined by high bias and low variance (Niang et al, 2021). Hence, the XGB can potentially help with learning better from an imbalanced distribution of input classes.…”

Section: Extreme Gradient Boosting (Xgb)mentioning

confidence: 99%

Coping with imbalanced data problem in digital mapping of soil classes

Sharififar

Sarmadian

2023

European J Soil Science

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An unsolved problem in the digital mapping of categorical soil variables and soil types is the imbalanced number of observations, which leads to reduced accuracy and the loss of the minority class (the class with a significantly lower number of observations compared to other classes) in the final map. So far, synthetic over-and under-sampling techniques have been explored in soil science; however, more efficient approaches that do not have the drawbacks of these techniques and guarantee retention of the minority classes in the produced map are essentially required. Such approaches suggested in the present study for digital mapping of soil classes include machine learning models of ensemble gradient boosting, cost-sensitive learning and one-class classification (OCC) of the minority class combined with multi-class classification. In this regard, extreme gradient boosting (XGB) as an ensemble gradient learner, a cost-sensitive decision tree (CSDT) within the C5.0 algorithm, and a one-class support vector machine combined with multi-class classification (OCCM) were investigated to map eight soil great groups with a naturally imbalanced frequency of observations in northwest Iran. A total of 453 profile data points were used for mapping the soil great groups of the study area. A data split was done manually for each class separately, which resulted in an overall 70% of the data for calibration and 30% for validation. The bootstrapping approach of calibration (with 10 runs) was performed to produce multiple maps for each model. The 10 bootstraps were evaluated against the hold-out validation dataset. The average values of accuracy measures, including Kappa (K), overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), were explored. In addition, the results of this study were compared with a previous study in the same area, in which resampling techniques were used to deal with imbalanced data for digital soil class mapping. The findings show that all three suggested methods can deal well with the imbalanced classification problem, with OCCM showing the highest K (= 0.76) and OA (= 82) in the validation stage. Also, this model can guarantee the retention of the minority classes in the final map. Comparing the present approaches with the previous study

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A Survey on Scalable Wireless Indoor Localization: Techniques, Approaches and Directions

Abraha,

Wang

2024

Wireless Pers Commun

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Comparison of Random Forest and Extreme Gradient Boosting Fingerprints to Enhance an indoor Wifi Localization System

Cited by 8 publications

References 11 publications

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Evaluation and Ranking of Artificial Intelligence Algorithms for Performance Prediction in Sheep

Coping with imbalanced data problem in digital mapping of soil classes

A Survey on Scalable Wireless Indoor Localization: Techniques, Approaches and Directions

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