DocumentCode :
714422
Title :
Classifier selection for RF based indoor positioning
Author :
Bozkurt, Sinem ; Gunal, Serkan ; Yayan, Ugur ; Bayar, Veli
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Eskisehir Osmangazi Univ., Eskişehir, Turkey
fYear :
2015
fDate :
16-19 May 2015
Firstpage :
791
Lastpage :
794
Abstract :
The selection of appropriate classifier is of great importance in improving the positioning accuracy and processing time for indoor positioning. In this work, an extensive analysis is carried out to determine the most appropriate classification algorithm to solve the indoor positioning problem. KIOS Research Center dataset is used in the experimental work. Principal Component Analysis method is employed together with Ranker method to determine the best features. In the next stage, the performances of Naïve Bayes, Bayesian Network, Multilayer Perceptron, K-Nearest Neighbor and J48 Decision Tree, which are widely preferred classification algorithms for indoor positioning studies, are analyzed on four distinct mobile phones. The results of the analysis reveal that J48 Decision Tree is superior to the other classification algorithms in terms of both processing time and accuracy.
Keywords :
Bayes methods; decision trees; indoor navigation; mobile radio; multilayer perceptrons; principal component analysis; Bayesian network; J48 decision tree; KIOS research center dataset; Naïve Bayes; RF based indoor positioning; Ranker method; appropriate classification algorithm; classifier selection; k-nearest neighbor; mobile phones; multilayer perceptron; positioning accuracy; principal component analysis; processing time; Bayes methods; Classification algorithms; Conferences; IEEE 802.11 Standards; Pervasive computing; Radar tracking; Radio frequency; Indoor positioning; RSSI; classification; feature extraction; feature selection; pattern and object recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
Type :
conf
DOI :
10.1109/SIU.2015.7129947
Filename :
7129947
Link To Document :
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