NDLI: INBS: An Improved Naive Bayes Simple learning approach for accurate indoor localization

Content Provider	IEEE Xplore Digital Library
Author	Wenzhe Zhang Lei Wang Zhenquan Qin Xueshu Zheng Liang Sun Naigao Jin Lei Shu
Copyright Year	2014
Description	Author affiliation: Guangdong Petrochem. Equip. Fault Diagnosis Key Lab., Guangdong Univ. of Petrochem. Technol., Maoming, China (Lei Shu) \|\| Sch. of Software, Dalian Univ. of Technol., Dalian, China (Wenzhe Zhang; Lei Wang; Zhenquan Qin; Xueshu Zheng; Liang Sun; Naigao Jin)
Abstract	Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering $560m^{2}$ in a campus building and a laboratory covering $78m^{2}.$ Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.
Starting Page	148
Ending Page	153
File Size	237329
Page Count	6
File Format	PDF
ISBN	9781479920037
DOI	10.1109/ICC.2014.6883310
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Accuracy Laboratories Buildings Training data Mathematical model IEEE 802.11 Standards
Content Type	Text
Resource Type	Article

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