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• W2920856984 abstract "An ensemble learning-based approach for context detection for high-speed railway (HSR) isproposed, evaluated, and compared against various machine learning algorithms.The context information is collected and formatted from the realistic physicaldata, which are measured by the field test in a commercial 4G cellular networkon-board high-speed train in an open area, HSR station, and typical urban area.The radio propagation knowledge implies that the channel model is implemented infeature extractions. The independent out-of-bag errors show ensemble learningapproaches; especially, random forests-based algorithm achieves very accuratecontext detection (up to 93.5%), which is much higher than single tree (66%).Other ensembles with sub-spaces of discriminant (67%) and K-nearest neighbour(69%), as well as linear discriminant analysis (55.7%), and support vectormachine (SVM) (27.7%). Furthermore, the features are selected based on thefeature importance evaluation in the first round training. The selectedpredominant features and the reasonable number of individual trees construct therefined random forests, which obtain a high accuracy (92.6%) and 70% timereducing. This experimental study benefits from physical radio knowledge tosupport advanced long-term evolution (LTE) network for railway and future smartrail applications." @default.
• W2920856984 created "2019-03-22" @default.
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• W2920856984 date "2019-05-07" @default.
• W2920856984 modified "2023-10-17" @default.
• W2920856984 title "Random forests‐enabled context detections for long‐term evolution network forrailway" @default.
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• W2920856984 doi "https://doi.org/10.1049/iet-map.2018.6025" @default.
• W2920856984 hasPublicationYear "2019" @default.
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