Multiple Tree Model Integration for Transportation Mode Recognition

被引：6
|
作者：
Ren, Yan [1 ]
机构：
[1] Renmin Univ China, Beijing, Peoples R China
来源：
UBICOMP/ISWC '21 ADJUNCT: PROCEEDINGS OF THE 2021 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2021 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2021年
关键词：
Transportation Mode Recognition; LightGBM; Random Forest; Bagging; Ensemble;
D O I：
10.1145/3460418.3479372
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
The team RY presents a solution for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge, which aims at differentiating eight transportation modes with mobile phone signal sensor data in this paper. This study first extracted a set of reasonable and discriminative features after data-preprocessing. Then, decision tree bagging, random forest, lightGBM are trained separately as basic models, whose predictions are integrated and afterward smoothed. The method gets 0.65 accuracy score on validation dataset.
引用
收藏
页码：385 / 389
页数：5
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