| 基于随机森林算法的多维情境特征活动识别 |
| |
| 引用本文： | 刘克强,汪云甲,陈锐志,褚天行.基于随机森林算法的多维情境特征活动识别[J].测绘通报,2017,0(7):29. |
| |
| 作者姓名： | 刘克强 汪云甲 陈锐志 褚天行 |
| |
| 作者单位： | 1. 中国矿业大学环境与测绘学院, 江苏 徐州 221116;2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;3. 德州农工大学科普斯分校地理空间计算实验室, 美国 德州 科普斯 TX78412 |
| |
| 基金项目： | 国家重点研发计划，国家高技术研究发展计划(863 计划)，现代工程测量国家测绘地理信息局重点实验室经费资助，2014江苏省普通高校研究生科研创新计划 |
| |
| 摘 要： | 利用智能手机传感器可感知时间、空间、时空和用户等多维情境的特征，可识别用户活动，但原框架模型中仅利用了单一分类器中的朴素贝叶斯算法，存在分类精度效果受限的问题。本文利用集成分类器中的随机森林算法对原有框架中的单一分类器进行了改进。在获取的3个数据集上的十倍交叉验证结果表明，加权平均F1量测值均有较大提高，表明利用随机森林算法在分类精度效果上有所提升；但由于集成算法结构相对复杂，其学习效率相对较低。此外，随机森林算法的分类混淆矩阵表明，导致识别误差的因素主要为活动的定义与室内定位精度。
|
| 关 键 词： | 活动识别 情境感知 机器学习 随机森林 手机传感器 |
| 收稿时间： | 2016-11-28 |
Research on Human Activity Recognition with Multiple Contexts by Using Random Forest |
| |
| LIU Keqiang,WANG Yunjia,CHEN Ruizhi,CHU Tianxing.Research on Human Activity Recognition with Multiple Contexts by Using Random Forest[J].Bulletin of Surveying and Mapping,2017,0(7):29. |
| |
| Authors: | LIU Keqiang WANG Yunjia CHEN Ruizhi CHU Tianxing |
| |
| Institution: | 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;2. State Key Labortatory of Information Engnineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;3. Conrad Blucher Institute for Surveying & Science, Texas A & M University Corpus Christi, Corpus Christi, TX 78412 USA |
| |
| Abstract: | Previous research has verified the effectiveness of a method which utilizes multiple contexts including temporal, spatial, spatiotemporal and user from smartphone sensors to recognize human activities. But it has limitation in classification accuracy performance when recognizing human activity on smartphones by using naïve Bayes. This paper presents an improving research work on recognizing activity by utilizing a random forest algorithm in ensemble learning instead of original naïve Bayes under the same framework. The result of a ten-fold cross-validation experiment on three subjects' datasets shows that the weighted average F1 measures are improved by using random forest. However, the training time efficiency measures are worse than naive Bayes because of the complex structure of random forest. Meanwhile, the confusion matrix shows that the main factors for classification error are activity definition and indoor positioning error. |
| |
| Keywords: | activity recognition context awareness machine learning random forest smartphone sensors |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《测绘通报》浏览原始摘要信息 |
| 点击此处可从《测绘通报》下载免费的PDF全文 |
|
