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- W2955420182 abstract "WIFI fingerprint positioning technology has been widely studied and developed for a long time and lots of experiment systems have been established. However, the time-varying and nonlinear features of the WIFI signal impede the development in the application level. The performance of existing positioning systems could be very unstable due to the signal varying. Our object is to combine the fuzzy technology and multi-classifier approach to improve the system accuracy and robustness. The proposed method adopts the fuzzy integral to fusion the results obtained from different fuzzy K-nearest neighbor (KNN) classifiers generated by adaBoost. Experiment results demonstrate that our approach improves the average positioning errors and their standard deviations by 21% and 26% separately compared to the traditional KNN algorithm." @default.
- W2955420182 created "2019-07-12" @default.
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- W2955420182 date "2019-01-01" @default.
- W2955420182 modified "2023-10-06" @default.
- W2955420182 title "A Multi-classifier Approach for Fuzzy KNN Based WIFI Indoor Localization" @default.
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- W2955420182 doi "https://doi.org/10.1007/978-3-030-22971-9_34" @default.
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