Ensemble learning model for Wifi indoor positioning systems
Abstract
WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for each position prediction model. The experimental result shows that our proposed ensemble model outperforms other compared models.
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PDFDOI: http://doi.org/10.11591/ijai.v10.i1.pp200-206
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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).