# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 4 Conclusions  
In this study, first of all, a more applicable, standard, and simple dataset structure was designed, and then a hierarchical structure of the new dataset as well as a higher performance method was introduced. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. According to the results obtained with the application of the method, the following conclusions were obtained. A simple, standardized, and more efficient dataset has been proposed instead of an indoor positioning dataset whose implementation is difficult and inefficient. This new, simple, and more standardized dataset that was proposed instead of the classic dataset has achieved high performance values. Also, a hierarchical fusion method was proposed to improve the classical ML models. The proposed HF method resulted in a significant improvement in floor and longitude estimation. The method was developed from the general to the specific using the Hierarchical structure of the localization process. Here, by transferring the estimation, it is ensured that the correct parts of the algorithms are strengthened. When the findings were examined, RF was found to be the most successful algorithm for building classification. In terms of accuracy metrics, the RF algorithm produces a value of 0.993 in training and a value of 0.995 in testing. Also, HF-RF is chosen as the most successful method for floor classification. In terms of accuracy metrics, the HF-RF algorithm produces a high score of 0.951 in training and 0.960 in testing. In terms of localization, this study concludes that the variable production and the HF method provide efficiency. The HF method has led to an improvement in the longitude estimation process, while it has also been shown to cause deterioration in the latitude estimation process. For this reason, in the estimation of longitude, HF-DT, which has a 3.751 MSE, was preferred and DT, which has a 3.751 MSE, was preferred in latitude estimation. In terms of positioning, this study concludes that the variable production and the HF method provide efficiency. Additionally, it was observed that the method of variable production proves to be successful. Further, it was concluded that the HF method only \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method [Citiation]** & **Longitude MSE** & **Latitude MSE** & **Longitude MAE** & **Latitude MAE** \\ \hline KNN [11] & 205.636 & 180.365 & - & - \\ SVM [11] & 252.81 & 127.238 & - & - \\ Extra-Trees [11] & 149.084 & 102.414 & - & - \\ HybLoc [14] & & & 6.46 & 6.46 \\ Proposed Longitude & 3.751 & - & 0.287 & 0.212 \\ Method (HF-DT) & & 2.171 & - & - \\ Proposed Method (DT) & - & 2.171 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of positioning studies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method [Citiation]** & **Building Accuracy** & **Floor Accuracy** & **Mean** \\ \hline
[12] & 0.99 & 0.98 & 0.985 \\
[52] & 0.98 & 0.92 & 0.95 \\ HybLoc [14] & 0.85 & 0.85 & 0.85 \\ Proposed Building Model (RF) & 0.99 & - & 0.97 \\ Proposed Floor Model (HF-RF) & - & 0.95 & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of localization studies. yields longitude estimation. Therefore, it is promising for the position estimation in indoor areas such as rooms, corridors, etc. Although there was no attempt to estimate the coordinates in any of the similar studies, a coordinate estimation was carried out in this study. However, the coordinate estimate was not as precise as GPS. This study serves as a preliminary study for future localization or indoor localization systems using Wi-Fi. Thanks to the results of this study, a strengthening technique with HF-ML method should provide higher performance results in similar hierarchical datasets.