全球定位系统
计算机科学
随机森林
人工智能
分类器(UML)
模式识别(心理学)
室内定位系统
特征(语言学)
集合(抽象数据类型)
职位(财务)
计算机视觉
数据挖掘
加速度计
哲学
经济
操作系统
电信
程序设计语言
语言学
财务
作者
Sae Iwata,Kohei Ishikawa,Toshinori Takayama,Masao Yanagisawa,Nozomu Togawa
标识
DOI:10.1109/icce-berlin.2018.8576188
摘要
Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this paper, we propose a robust indoor/outdoor detection method based on sparse GPS positioning information utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown sequence of measured positions into indoor/outdoor positions using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the F 1 measure of 0.9836, which classifies measured positions into indoor/outdoor ones with almost no errors.
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