Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures

Georgy Minaev; Philipp Müller; Ari Visa; Robert Piché

doi:10.1109/ICoSC.2018.8587811

Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures

Georgy Minaev, Philipp Müller, Ari Visa, Robert Piché

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

35 Downloads (Pure)

Abstract

Measurements from an ion mobility spectrometry electronic nose (eNose) can be used for distinguishing different rooms in indoor localisation. An earlier study showed that the Nearest Neighbour classifier with Euclidean distance for features provides reasonable accuracy under certain conditions. In this paper 66 alternative distance measures are compared to the Euclidean distance and principal component analysis (PCA) is applied to the data. PCA shows that the measurements on the various channels of the eNose are correlated and that using principal components 1, 2 and 4 increases the accuracy considerably. Furthermore, the experiments revealed three Pareto optimal distance measures that reduce the misclassification rate between 9-10% while using only 82-88% of the search time compared with Euclidean distance.

Original language	English
Title of host publication	2018 7th International Conference on Systems and Control (ICSC)
Subtitle of host publication	24-26 Oct. 2018, Valencia, Spain
Place of Publication	Valencia, Spain
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	978-1-5386-8537-2
ISBN (Print)	978-1-5386-8538-9
DOIs	https://doi.org/10.1109/ICoSC.2018.8587811
Publication status	Published - Oct 2018
Publication type	A4 Article in conference proceedings
Event	International Conference on Systems and Control - Duration: 1 Jan 2000 → …

Publication series

Name
ISSN (Electronic)	2379-0067

Conference

Conference	International Conference on Systems and Control
Period	1/01/00 → …

Publication forum classification

Publication forum level 1

Access to Document

10.1109/ICoSC.2018.8587811

MinaevCameraICSC2018
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Accepted author manuscript, 1.16 MB

http://urn.fi/URN:NBN:fi:tty-201901231140

Cite this

@inproceedings{4ebdaa8ed7114e7ea8976ecd3c04185f,

title = "Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures",

abstract = "Measurements from an ion mobility spectrometry electronic nose (eNose) can be used for distinguishing different rooms in indoor localisation. An earlier study showed that the Nearest Neighbour classifier with Euclidean distance for features provides reasonable accuracy under certain conditions. In this paper 66 alternative distance measures are compared to the Euclidean distance and principal component analysis (PCA) is applied to the data. PCA shows that the measurements on the various channels of the eNose are correlated and that using principal components 1, 2 and 4 increases the accuracy considerably. Furthermore, the experiments revealed three Pareto optimal distance measures that reduce the misclassification rate between 9-10% while using only 82-88% of the search time compared with Euclidean distance.",

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isbn = "978-1-5386-8538-9",

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Minaev, G, Müller, P , Visa, A & Piché, R 2018, Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures. in 2018 7th International Conference on Systems and Control (ICSC): 24-26 Oct. 2018, Valencia, Spain. IEEE, Valencia, Spain, International Conference on Systems and Control, 1/01/00. https://doi.org/10.1109/ICoSC.2018.8587811

Indoor Localisation using Aroma Fingerprints: Comparing Nearest Neighbour Classification Accuracy using Different Distance Measures. / Minaev, Georgy; Müller, Philipp ; Visa, Ari et al.
2018 7th International Conference on Systems and Control (ICSC): 24-26 Oct. 2018, Valencia, Spain. Valencia, Spain: IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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N2 - Measurements from an ion mobility spectrometry electronic nose (eNose) can be used for distinguishing different rooms in indoor localisation. An earlier study showed that the Nearest Neighbour classifier with Euclidean distance for features provides reasonable accuracy under certain conditions. In this paper 66 alternative distance measures are compared to the Euclidean distance and principal component analysis (PCA) is applied to the data. PCA shows that the measurements on the various channels of the eNose are correlated and that using principal components 1, 2 and 4 increases the accuracy considerably. Furthermore, the experiments revealed three Pareto optimal distance measures that reduce the misclassification rate between 9-10% while using only 82-88% of the search time compared with Euclidean distance.

AB - Measurements from an ion mobility spectrometry electronic nose (eNose) can be used for distinguishing different rooms in indoor localisation. An earlier study showed that the Nearest Neighbour classifier with Euclidean distance for features provides reasonable accuracy under certain conditions. In this paper 66 alternative distance measures are compared to the Euclidean distance and principal component analysis (PCA) is applied to the data. PCA shows that the measurements on the various channels of the eNose are correlated and that using principal components 1, 2 and 4 increases the accuracy considerably. Furthermore, the experiments revealed three Pareto optimal distance measures that reduce the misclassification rate between 9-10% while using only 82-88% of the search time compared with Euclidean distance.

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