A case study of optimal decision tree construction for RFKON database

The estimation of user position in indoor environment using WLAN technology based on Received Signal Strength (RSS) is becoming increasingly important in recent years. Various indoor positioning techniques are proposed in the literature. Fingerprint positioning technique is the most promising one that consists of radio frequency (RF) map construction and location estimation phases. Machine learning algorithms are used in the location estimation phase. Decision Tree algorithm is one of the most commonly applied ML algorithm that is used to infer user position by researchers. In this case study, we analyze decision tree algorithm parameters to find an optimal decision tree for indoor positioning system. The accuracy of this optimal tree is analyzed in the experiments.