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Labeling sensing data for mobility modeling
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DOI：10.1016/j.is.2015.09.001.png)
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Abstract
En 中文
In urban environments, sensory data can be used to create personalized models for predicting efficient routes and schedules on a daily basis; and also at the city level to manage and plan more efficient transport, and schedule maintenance and events. Raw sensory data is typically collected as time-stamped sequences of records, with additional activity annotations by a human, but in machine learning, predictive models view data as labeled instances, and depend upon reliable labels for learning. In real-world sensor applications, human annotations are inherently sparse and noisy. This paper presents a methodology for preprocessing sensory data for predictive modeling in particular with respect to creating reliable labeled instances. We analyze real-world scenarios and the specific problems they entail, and experiment with different approaches, showing that a relatively simple framework can ensure quality labeled data for supervised learning. We conclude the study with recommendations to practitioners and a, discussion of future challenges. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Sensory data
Sensor fusion
Hidden Markov models
Multi-label
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