TY  - JOUR
TT  - A Hybrid Approach for Indoor Positioning
AU  - Bozkurt Keser, Sinem
AU  - Yayan, Uğur
PY  - 2016
DA  - December
DO  - 10.18201/ijisae.270409
JF  - International Journal of Intelligent Systems and Applications in Engineering
PB  - İsmail SARITAŞ
WT  - DergiPark
SN  - 2147-6799
SP  - 162
EP  - 165
VL  - 4
IS  - Special Issue-1
KW  - Fingerprinting
KW  - indoor positioning
KW  - access point selection
KW  - clustering
KW  - classification
KW  - feature selection
KW  - expectation maximization
KW  - decision tree
KW  - received signal strength
N2  - Positioning systems have wide rangeof applications with the developing technology. Global Positioning System (GPS)is an efficient solution for outdoor applications but it gives poor accuracy inindoor environment. And, various methods are proposed in the literature such asgeometric-based, fingerprint-based, etc. In this study, a hybrid approach thatuses both clustering and classification is developed for fingerprint-basedmethod. Information gain based feature selection method is used for selectionof the most appropriate features from the WiFi fingerprint dataset in theinitial step of this approach. Then, Expectation Maximization (EM) algorithm isapplied for clustering purpose. Then, decision tree algorithm is used as aclassification task for each cluster. Experimental results indicate thatapplied algorithms lead to a substantial improvement on localization accuracy.Since, cluster specific decision tree models reduce the size of the tree significantly;computational time of position phase is also reduced.
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UR  - https://doi.org/10.18201/ijisae.270409
L1  - https://dergipark.org.tr/en/download/article-file/236982
ER  -