# A Narrow-Down Approach Based on Machine Learning for Indoor Localization
## 2 Related Work  
In the literature, many Wi-Fi RSSI-based machine learning (ML) approaches have been proposed for indoor localization. For example, [PERSON] et al. [3] proposed a zone-based indoor localization system using neural networks with a slight modification in traditional counter propagation network (CPN). The proposed scheme reduces the number of empty clusters and performs better than the basic CPN by increasing 1% in accuracy. However, it is lacking in finding the exact user's location coordinates. A hidden Markov model-based indoor localization scheme is proposed in [4], but the random forest algorithm outperforms the proposed method. [PERSON] et al. [5] proposed a Wi-Fi RSSI-based indoor robot positioning system that is pluggable to existing Wi-Fi network infrastructures. They integrated the deep neural network with fuzzy forests to increase accuracy. If we use both the RSSI's value and direction to train the random forest algorithm, then its accuracy can be increased [13]. [PERSON] et al. [14] proposed an algorithm to divide the whole localization area into sub-areas by using the Gaussian mixture model. RF algorithm was utilized to predict the corresponding area and the final location was estimated using an adaptive KNN algorithm. [PERSON] al. [15] utilized a deep learning framework alongside a logistic regression algorithm and [PERSON] et al. [16] utilized the K-means clustering algorithm to divide the localization area into different sets of log-distance propagation models, while Bayesian inference improves the positioning accuracy. A random forest algorithm using a software-defined network (SDN) framework is presented in [17]. The proposed model uses cross-validation for training and performing indoor localization. [PERSON] et al. [18] proposed a novel adaptive cluster splitting (ACS) and access point (AP) reselection scheme in each sub-cluster splitting process. In the online phase, a decision tree-based exhaustive search algorithm finds the user's location. [PERSON] et al. [19] proposed an algorithm consisting of clustering and searching. A measuring device determines the user's location, based on the strongest AP, in a radio map. An AP's similarity-based clustering approach is proposed in [20]. [PERSON] et al. [21] proposed a heterogeneous knowledge transfer framework for fingerprinting-based indoor localization. After removing the redundant knowledge in the source domain, the authors derived a cross-domain mapping to construct a homogeneous feature space, where they combined the mapping and weights learning into a joint objective function and solved it using a three-step iterative optimization algorithm. However, they utilized online fingerprint knowledge to train a model that makes this approach less realistic. [PERSON] et al. [22] presented a robust model by fusing derivative fingerprints of RSS with multiple classifiers (DIFMICs). This model outperforms many machine learning-based models proposed in the literature for indoor localization. [PERSON] et al. [23] proposed a probabilistic model to intelligently estimate the user's location by evaluating the label's credibility. [PERSON] et al. [24] presented a hybrid localization model by joining the convolutional neural network (CNN) and Gaussian process regression (GPR) algorithms. The hybrid model improved in performance by 45.8% and the GPR algorithm further increased the localization accuracy. [PERSON] et al. [25] proposed a wavelet scattering framework (WSF)-based neural network for an indoor localization method that is not affected by the handset orientation, and [PERSON] et al. [26] proposed a hybrid fingerprint quality evaluation model (HFQEM) that can find the location by evaluating the hybrid fingerprint quality in different sub-areas. The authors of [27] present a sequence learning problem, where a recurrent neural network (RNN) with a regression output are used to estimate three-dimensional positions. The authors of [28] propose a convolutional neural network (CNN) model based on RSSI fingerprint datasets.