One-to-all regularized logistic regression-based classification for WiFi indoor localization

Peng, Zifan; Xie, Yuchen; Wang, Donglin; Dong, Ziqian

doi:10.1109/sarnof.2016.7846746

Cited by 6 publications

(2 citation statements)

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“…It was an increase in accuracy by more than 20% when compared with the fingerprint positioning method, and improvement in performance by more than 15% when compared with DT and RF. Logistic Regression (LR) was employed by the authors in [20]. A 95.83% accuracy was obtained after data optimization, which was an increase of 80% greater than K-Mean.…”

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: Related Workmentioning

confidence: 99%

“…In this research, we propose a feature reduction method and an indoor localization method using a machine learning models that combine the algorithms of KNN [14,15], SVM [16], RF [17], ExtraTree [18] , LGBM [19], LR [20], and Linear Regression (LiR) [21]. We use the classification problem to predict the floor number.…”

Section: Introductionmentioning

confidence: 99%