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

页码：385 / 389

页数：5

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