Intelligent Indoor Positioning Systems: The Case of Imbalanced Data

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorMoussa, Sherif
dc.contributor.authorReyes, Jorge Avante
dc.date.accessioned2023-10-09T16:46:28Z
dc.date.available2023-10-09T16:46:28Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractThe 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.
dc.identifier.citationKamalov, 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_49
dc.identifier.issn23674512
dc.identifier.urihttps://doi.org/10.1007/978-981-99-1767-9_49
dc.identifier.urihttps://hdl.handle.net/20.500.12519/893
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseriesIntelligent Communication Technologies and Virtual Mobile Networks. ICICV 2023. Lecture Notes on Data Engineering and Communications Technologies; Volume 171
dc.rightsLicense to reuse abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
dc.subjectImbalanced data
dc.subjectIndoor positioning systems
dc.subjectIntelligent systems
dc.subjectMachine learning
dc.titleIntelligent Indoor Positioning Systems: The Case of Imbalanced Data
dc.typeBook chapter

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