# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 4 Experimental Results and Discussions  
In this study, we utilized classical ML algorithms, such as Fine Tree, k-NN, SVM, Wide Neural Network, Linear Discriminant, and Ensemble Bagged Trees, due to their relative simplicity, interpretability, and effectiveness when applied to small-to-medium-sized datasets. These methods are particularly well-suited for problems with structured, tabular data like Wi-Fi fingerprints combined with the room-to-room transition time feature. Classical ML approaches also allowed us to focus on exploring the potential of the novel temporal feature we introduced, without the additional complexity of training and optimizing deep learning models. To evaluate the performances of various ML algorithms, Accuracy, Precision, Recall, and F1 score metrics were obtained by applying 10-fold cross-validation to the three datasets collected from the different people. These methods were applied to both the raw Wi-Fi dataset and the combined Wi-Fi+Room-to-Room Transition Time datasets, to examine the effect of room-to-room transition time information on the accuracy of the models. Tables 5-7 compare the accuracy scores obtained by classifying the two datasets. Similarly, Precision, Recall, and F1 score values were evaluated for all generated datasets, as shown in Table 8-10. The metrics for measuring the performance of the models are described below:  
* _Accuracy._ This metric was actually the ratio of the number of data for which labels were correctly predicted to the number of all predictions performed by the model: \[\text{Accuracy}=\frac{\text{True Positives}+\text{True Negatives}}{\text{Total Number of Predictions}}\] Although the accuracy metric provided crucial information about the performance of the model, metrics such as Precision, Recall, and F1 score were needed when the number of classes by label type was not equal. * _Precision_: This metric gives the proportion of True Positives predicted out of all positive predictions made by a model, whether true or false. The use of this metric is important when False Positive predictions have a high impact on the performance of the model: \[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}}\] If the model has high precision, it means that the model has lower False Positive errors compared to models that have lower precision. For the dataset used in this study, an example of a False Positive is when a dataset that was actually labeled as \"LivingRoom\" was classified as another room name. * _Recall_: The sensitivity metric is the ratio of True Positives over False Positives to the sum of True Positive and False Negative predictions: \[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}\] If a model has a high recall, it can effectively predict positive samples while minimizing False Negative predictions. * _F1 score_: The F1 score metric considers both Precision and Recall metrics and calculates the harmonic mean of both metrics. Since this metric combines both Precision and Recall metrics in a single equation, it is important to consider when the class distribution is unbalanced:  
\[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\]  
A model with a high F1 score metric represents an efficient classifier, as it minimizes both False Positives and False Negatives. In the evaluation process of the datasets created in this study, the model was trained on the training data by dividing the datasets with 10-fold cross-validation for each algorithm, and the performances of the obtained model on the test sets were compared. The use of 10-fold cross-validation showed that the results did not vary depending on the test set. Tables 5-7 show a comparison of the accuracy rates for the Wi-Fi dataset versus Wi-Fi + Room-to-Room (Wi-Fi + RR) in Datasets 1, 2, and 3, respectively. The findings provide promising insights into how including room-to-room transition time data affects results.