  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.
