In this paper, we propose a novel Fine-Tuned attribute Weighted Na\"ive Bayes
(FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight
(NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System
(IPS). The FT-WNB classifier assigns each signal feature a specific weight and
fine-tunes its probabilities to address the mismatch between the predicted and
actual class. The performance of the FT-WNB classifier is compared with the
state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy
Maximum Relevance (mRMR)- $k$-Nearest Neighbour (KNN), Support Vector Machine
(SVM), Decision Tree (DT), Na\"ive Bayes (NB), and Neural Network (NN). It is
demonstrated that the proposed classifier outperforms other algorithms by
achieving a high NLoS classification accuracy of $99.7\%$ with imbalanced data
and $99.8\%$ with balanced data. The experimental results indicate that our
proposed FT-WNB classifier significantly outperforms the existing
state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered
scenario.

提出了一种新颖的 Fine-Tuned attribute Weighted Naive Bayes (FT-WNB) 分类器以用于室内定位系统中的 UltraWide Bandwidth 信号的 Line-of-Sight (LoS) 和 Non-Line-of-Sight (NLoS) 的识别，其性能比现有的最先进的机器学习方法在该场景下显著更好。