JLGBMLoc—A Novel High-Precision Indoor Localization Method Based on LightGBM

Yin, Lu; Ma, Pengcheng; Deng, Zhongliang

doi:10.3390/s21082722

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…Accordingly, our model has superior accuracy than majority of other models. The results of the study in [38] were better than ours. The distinction is that the authors of that study divided the training dataset into two datasets (training and testing) in an 80/20 ratio and their goal was room-level positioning.…”

Section: Evaluation Of the Floor Prediction Resultscontrasting

confidence: 70%

“…The charts in Figure 17 show that our model has better results than other studies. Noticeably, the accuracy by the position of our proposed model is higher than that of the study in [38].…”

Section: Evaluation Of the Results Of Longitude And Latitude Estimatescontrasting

confidence: 68%

“…The RF algorithm used to predict the floor number had an accuracy of 97%. Liye Zhang et al divided the training dataset into two parts which were 80% for training and 20% for testing as they aimed for room-level positioning [38]. The team proposed a novel feature extraction algorithm named JLGBMLoc (Joint Denoising Auto-encoder (JDAE) with LGBM algorithm).…”

Section: Related Workmentioning

confidence: 99%

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Two-Phase Combined Model to Improve the Accuracy of Indoor Location Fingerprinting

Vu,

Ngo-Van,

Hoang Do Thanh

2022

JCC

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Wi-Fi Fingerprinting based Indoor Positioning System (IPS) aims to help locate and navigate users inside buildings. It has become a popular research topic in recent years. For the most parts, authors use the traditional machine learning algorithms to enhance the accuracy of locationing. Their methods involve using a standalone algorithm or a combination of different algorithms in only one phase, producing results with an acceptable accuracy. In this paper, we present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduction method. Specifically, our research is focused on the combination of different regression and classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extra Tree Regressor (extraTree), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR) and Linear Regression (LiR) to create a new data set and models that can be used in the training phase. These proposed models are tested on the UJIIndoorLoc 1 dataset. Our experimental results show a prediction accuracy of 98.73% by floor, and an estimated accuracy of 99.62% and 99.52% respectively by longitude and latitude. When compared with the results of the models in which we use independent algorithms, and of other researches that have different models using the same algorithms and on the same dataset, most of our results are better.

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Section: Evaluation Of the Floor Prediction Resultscontrasting

confidence: 70%

“…The charts in Figure 17 show that our model has better results than other studies. Noticeably, the accuracy by the position of our proposed model is higher than that of the study in [38].…”

Section: Evaluation Of the Results Of Longitude And Latitude Estimatescontrasting

confidence: 68%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Two-Phase Combined Model to Improve the Accuracy of Indoor Location Fingerprinting

Vu,

Ngo-Van,

Hoang Do Thanh

2022

JCC

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“…Nowadays, for classification and diagnosis problems, LightGBM outperforms other state-of-the-art methods, cf. [33][34][35][36][37][38][39][40]. In these related works, LightGBM is not only selected for its effective prediction performance, but also for its shorter computational time and optimized data handling technique.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Rufo

Debelee

Ibenthal³

et al. 2021

Diagnostics

137

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Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.

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