Kamalov, FiruzMoussa, SherifReyes, Jorge Avante2023-10-092023-10-09© 20232023Kamalov, F., Moussa, S. & Reyes, J.A. (2023). Intelligent Indoor Positioning Systems: The Case of Imbalanced Data. In G. Rajakumar, KL Du, & A. Rocha (Eds.) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies, 171 (pp. 677 - 686). Springer, Singapore. https://doi.org/10.1007/978-981-99-1767-9_4923674512https://doi.org/10.1007/978-981-99-1767-9_49https://hdl.handle.net/20.500.12519/893The ubiquity of Wi-Fi over the last decade has led to increased popularity of intelligent indoor positioning systems (IPS). In particular, machine learning has been recently utilized to develop intelligent IPS. Most of the existing research focus on developing intelligent IPS using balanced data. In this paper, we investigate a hitherto unexamined issue of imbalanced data in the context of machine learning-based IPS. We consider several traditional machine learning algorithms to determine the optimal method for training IPS on imbalanced data. We also analyze the effect of imbalance ratio on the performance of the IPS. The results show that the k-nearest neighbors algorithm provides the best approach to developing intelligent IPS for imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.enLicense to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.Imbalanced dataIndoor positioning systemsIntelligent systemsMachine learningBook chapterCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.