Abstract : We proposed the first Conformal Prediction (CP) algorithm for indoor localisation with a classification approach. The algorithm can provide a region of predicted locations, and a reliability measurement for each prediction. However, one of the shortcomings of the former approach was the individual treatment of each dimension. In reality, the training database usually contains multiple signal readings at each location, which can be used to improve the prediction accuracy. In this paper, we enhance our former CP with the Kullback-Leibler divergence, and propose two new classification CPs. The empirical studies show that our new CPs performed slightly better than the previous CP when the resolution and density of the training database are high. However, the new CPs performs much better than the old CP when the resolution and density are low.
Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.411-420, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_42〉
https://hal.inria.fr/hal-01459636
Contributeur : Hal Ifip
<>
Soumis le : mardi 7 février 2017 - 13:05:55
Dernière modification le : vendredi 1 décembre 2017 - 01:16:34
Document(s) archivé(s) le : lundi 8 mai 2017 - 14:14:41
Khuong Nguyen, Zhiyuan Luo. Enhanced Conformal Predictors for Indoor Localisation Based on Fingerprinting Method. Harris Papadopoulos; Andreas S. Andreou; Lazaros Iliadis; Ilias Maglogiannis. 9th Artificial Intelligence Applications and Innovations (AIAI), Sep 2013, Paphos, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-412, pp.411-420, 2013, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-41142-7_42〉. 〈hal-01459636〉
