XGBoost and LightGBM A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation
DOI:
https://doi.org/10.22399/ijcesen.329Keywords:
Human Activity Recognition, LightGBM, XGBoostAbstract
Human activity recognition is the process of automatically identifying and classifying human activities based on data collected from different modalities such as wearable sensors, smartphones, or similar devices having necessary sensors or cameras capturing the behavior of the individuals. In this study, XGBoost and LightGBM approaches for human activity recognition are proposed and the performance and execution times of the proposed approaches are compared. The proposed methods on a dataset including accelerometer and gyroscope data acquired using a smartphone for six activities. The activities are namely laying, sitting, standing, walking, walking downstairs, and walking upstairs. The available dataset is divided into training and test sets, and proposed methods are trained using the training set, and tested on the test sets. At the end of the study, 97.23% accuracy using the LightGBM approach, and 96.67% accuracy using XGBoost is achieved. It is also found that XGBoost is faster than the LightGBM, whenever the execution times are compared.
References
Saravanan, S., Ramkumar, K., Adalarasu, K. et al. (2022). A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease. Arch Computat Methods Eng 29;3639–3653. https://doi.org/10.1007/s11831-022-09710-1
Grueso, S., Viejo-Sobera, R. (2021). Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alz Res Therapy 13; 162. https://doi.org/10.1186/s13195-021-00900-w
Siddiqui, S. Y., Athar, A., Khan, M. A., Abbas, S., Saeed, Y., Khan, M. F., & Hussain, M. (2020). Modelling, simulation and optimization of diagnosis cardiovascular disease using computational intelligence approaches. Journal of Medical Imaging and Health Informatics, 10(5), 1005-1022.
Kwapisz, J.R., Weiss, G.M., & Moore, S.A. (2011). “Activity recognition using cell phone accelerometers”, ACM SigKDD Explorations Newsletter, 12(2), 74-82.
Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., & Havinga, P.J. (2014). “Fusion of smartphone motion sensors for physical activity recognition”, Sensors, 14(6), 10146-10176.
Bao, L., & Intille, S.S. ,(2004). “Activity recognition from user-annotated acceleration data”. In Proceedings of the International Conference on Pervasive Computing, Berlin, 1-17.
El Marhraoui, Y., Amroun, H., Boukallel, M., Anastassova, M., Lamy, S., Bouilland, S., & Ammi, M, (2022). Foot-to-Ground Phases Detection: A Comparison of Data Representation Formatting Methods with Respect to Adaption of Deep Learning Architectures, Computers, 11(5);58. MDPI AG.
Li, K., Habre, R., Deng, H., Urman, R., Morrison, J., Gilliland, F. D., Ambite, J. L., Stripelis, D., Chiang, Y. Y., Lin, Y., Bui, A. A., King, C., Hosseini, A., Vliet, E. V., Sarrafzadeh, M., & Eckel, S. P., (2019). Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data, JMIR mHealth and uHealth, 7(2), e11201.
Xu, S., Tang, Q., Jin, L., & Pan, Z. (2019). A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones”, Sensors, 19(10), 2307. MDPI AG.
Yousif, H., & Abdulah, D., (2022). Evaluation of machine learning approaches for sensor-based human activity recognition, International Journal of Nonlinear Analysis and Applications, 13(2), 1183-1200.
Csizmadia, G., Liszkai-Peres, K., Ferdinandy, B. et al., (2022) Human activity recognition of children with wearable devices using LightGBM machine learning, Sci Rep, 12, 5472.
Mutegeki, R., & Han, D. S., “A CNN-LSTM approach to human activity recognition”, international conference on artificial intelligence in information and communication (ICAIIC), Fukuoka, Japan, 362-366, 2020.
Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., Ryan, M., Elhoseny, M., & Song, H., (2020) “ST-DeepHAR: Deep learning model for human activity recognition in IoHT applications”, IEEE Internet of Things Journal, 8(6), 4969-4979.
Ronao, C. A., & Cho, S. B., “Deep convolutional neural networks for human activity recognition with smartphone sensors”, In Neural Information Processing: 22nd International Conference, ICONIP, 4(22), 46-53, 2015.
Wan, S., Qi, L., Xu, X., Tong, C., & Gu, Z., (2020). Deep learning models for real-time human activity recognition with smartphones”, Mobile Networks and Applications, 25, 743-755.
Guha, R., Khan, A. H., Singh, P. K., Sarkar, R., & Bhattacharjee, D., (2021). CGA: A new feature selection model for visual human action recognition, Neural Computing and Applications, 33, 5267-5286.
Bhattacharya, D., Sharma, D., Kim, W., Ijaz, M. F., & Singh, P. K., (2022). Ensem-HAR: An ensemble deep learning model for smartphone sensor-based human activity recognition for measurement of elderly health monitoring, Biosensors, 12(6), 393.
Sengul G., Ozcelik, E., Misra, S., Damaševičius, R., & Maskeliūnas, R., (2021). Fusion of smartphone sensor data for classification of daily user activities”, Multimedia Tools and Applications, 80, 33527–33546.
Zhang, W., Zhao, X., And Lı, Z., (2019). A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost”, IEEE Access, (7), 80027-80042.
Shafique, M., S., and Marchán, S., S, (2022). Investigating the Impact of Information Sharing in Human Activity Recognition”, Sensors, 22, 2280.
Syed, A., S., Syed, Z. S., Shah, M., S. and Saddar, S., (2020). Using Wearable Sensors for Human Activity Recognition in Logistics: A Comparison of Different Feature Sets and Machine Learning Algorithms International Journal of Advanced Computer Science and Applications (IJACSA), 11(9).
Gao, X.; Luo, H.; Wang, Q.; Zhao, F.; Ye, L.; Zhang, Y., (2019). A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM, Sensors, 19, 947
Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J.L. (2013). A Public Domain Dataset for Human Activity Recognition using Smartphones. The European Symposium on Artificial Neural Networks.
Chen, T., & Guestrin, C., “XGBoost: A Scalable Tree Boosting System”, In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794, 2016.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., & Liu, T. Y., “LightGBM: a highly efficient gradient boosting decision tree”, In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 3149–3157, 2017.
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