TY  - JOUR
AU  - Padmaja, Budi 
AU  - Vaddella, Venkata Rama Prasad 
AU  - Sunitha, Kota Venkata Naga 
PY  - 2019
TI  - Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset
JF  - Journal of Computer Science
VL  - 15
IS  - 5
DO  - 10.3844/jcssp.2019.673.680
UR  - https://thescipub.com/abstract/jcssp.2019.673.680
AB  - Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings. The prevalent pattern recognition methodologies have made immense progress over recent years. Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization. This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data. We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting. Later, we compared the performance of proposed procedure with some existing approaches. It is observed that our adopted procedure outperforms with comparatively better statistical performance measures. It also gained 4.7x speed up in computation.