Feature selection on database optimization for Wi-Fi fingerprint indoor positioning

Author:

Apostolo Guilherme Henrique,Sampaio Igor Garcia Ballhausen,Viterbo José

Publisher

Elsevier BV

Subject

General Engineering

Reference14 articles.

1. A comparative study on machine learning algorithms for indoor positioning, in: Innovations in Intelligent SysTems and Applications (INISTA), 2015 International Symposium on;Bozkurt,2015

2. Removing useless aps and fingerprints from wifi indoor positioning radio maps, in: Indoor Positioning and Indoor Navigation (IPIN), 2013 International Conference on;Eisa,2013

3. Goswami, A., Ortiz, L.E., Das, S.R., 2011. Wigem: A learning-based approach for indoor localization, in: Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies, ACM. p. 3.

4. The WEKA data mining software: an update;Hall;SIGKDD Explorations,2009

5. Wi-fi fingerprint-based indoor positioning: Recent advances and comparisons;He;IEEE Communications Surveys & Tutorials,2016

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1. Selection of Signal Sources Influence at Indoor Positioning System;IEEE Transactions on Wireless Communications;2024-01

2. Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an Outlook;IEEE Open Journal of the Communications Society;2023

3. Hierarchical fingerprinting and feature extraction for indoor localization;ICC 2022 - IEEE International Conference on Communications;2022-05-16

4. SMOTE for Wi-Fi Fingerprint Construction in Indoor Positioning Systems;2021 IEEE International Performance, Computing, and Communications Conference (IPCCC);2021-10-29

5. The Analysis of Feature Selection with Machine Learning for Indoor Positioning;2021 29th Signal Processing and Communications Applications Conference (SIU);2021-06-09

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