# Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization
## 1 Introduction
Moreover, initiatives inspired by these studies such as Insiteo [15], Wifarer [16], Insoft [17], The Framework for Internal Navigation and Discovery (FIND) [18], and MapsIndoors [19] were launched as well. Successful studies with Wi-Fi fingerprint positioning are found in various areas of subjects today [12; 20]. One of the most recent datasets for Wi-Fi fingerprint-based localization is named \"UJIndoorLoc.\" This dataset is obtained by [PERSON] et al. and makes use of diverse types of studies [12; 21; 22]. These studies are mainly focused on adopting the same 525 features. In the process of classification and regression, it is challenging, time-consuming, and inefficient to apply all the input values directly to learning algorithms. One of the preferred methods in data reduction is principal component analysis (PCA) [23]. Some researchers use PCA and/or linear discriminant analysis (LDA) to reduce the data features [24]. The total number of features for these studies is greater than 100, even when sufficient raw features are covered by the reduced feature set. Since the number of wireless access points (WAP) may vary according to the environment of application and too many features may be employed, it is difficult and inefficient to implement it for real-world indoor positioning. Using the PCA method, new components are created according to the feature set, and it is possible to cover all of the features. However, the PCA method is specifically designed for the structure of that dataset. In this regard, if the Wi-Fi infrastructure is changed, the PCA-generated system will become useless because of the need to change the properties of its components. The dataset that is to be used in real-world applications should have common features found in every environment while also being compatible with the changing number of WAP and containing few features. Investigating the previous studies, it was observed that a large number of features were used while the existing dataset was used without adequate simplification. Prominent studies were also found in the literature; however, these were either not practical or only valid for specific cases. Thus, it was not possible to provide an application with common parameters found in every environment. The main purpose of this study is to develop a method that can be applied for any environment where WAP mapping data are collected. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. In order to achieve this goal, machine learning (ML) methods are compared and the results of successful ML methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from \"UJIIndoorLoc\" dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. The second part of the paper introduces the dataset adopted in this study and the method of positioning. The third part presents the results of the study. Finally, a conclusion is suggested according to the results. The main contribution of this study will be to propose a more general dataset structure for datasets such as localization and positioning in the hierarchical structure and to provide a more successful retrofit method in hierarchical situations.