Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying

https://doi.org/10.22146/ijccs.73179

Supriyadi La Wungo(1), Firman Aziz(2*)

(1) Informatics Engineering, STMIK Kreatindo, Monokwari
(2) Universitas Pancasakti
(*) Corresponding Author

Abstract


The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.


Keywords


Physical Activity; Classification; Multiclass Ensemble GradientBoost; Newton Rapshon Parameter; Smartphone

Full Text:

PDF


References

[1] C. Bouchard, S. Blair, and W. Haskell, Physical activity and health. 2012.

[2] J. Thomas, P. Martin, J. Etnier, and S. Silverman, Research methods in physical activity. 2022.

[3] B. Do et al., Physical activity epidemiology. 2022.

[4] N. Islam and R. Want, “Smartphones: Past, present, and future,” IEEE Pervasive Comput., vol. 13, no. 4, pp. 89–92, Oct. 2014, doi: 10.1109/MPRV.2014.74.

[5] M. Khamis, F. Alt, … A. B. the 20th I. C. on, and undefined 2018, “The past, present, and future of gaze-enabled handheld mobile devices: Survey and lessons learned,” dl.acm.org, p. 9781450358989, Sep. 2018, doi: 10.1145/3229434.3229452.

[6] M. Liu, “A study of mobile sensing using smartphones,” Int. J. Distrib. Sens. Networks, vol. 2013, 2013, doi: 10.1155/2013/272916.

[7] A. Lawi, F. Aziz, and S. L. Wungo, “Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method,” J. Phys. Conf. Ser., vol. 1341, no. 4, p. 042002, Oct. 2019, doi: 10.1088/1742-6596/1341/4/042002.

[8] R. Voicu, C. Dobre, L. Bajenaru, R. C.- Sensors, and undefined 2019, “Human physical activity recognition using smartphone sensors,” mdpi.com, doi: 10.3390/s19030458.

[9] M. Chen, J. Han, P. Y.-I. T. on Knowledge, and undefined 1996, “Data mining: an overview from a database perspective,” ieeexplore.ieee.org, Accessed: Jun. 15, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/553155/.

[10] M. Islam, M. Hasan, X. Wang, H. G.- Healthcare, and undefined 2018, “A systematic review on healthcare analytics: application and theoretical perspective of data mining,” mdpi.com, Accessed: Jun. 29, 2022. [Online]. Available: https://www.mdpi.com/2227-9032/6/2/54.

[11] O. Sheredeko and O. A.-М. першої міжнар. наук.-практ. конф.«, “DATA MINING: AN OVERVIEW,” dspace.nuft.edu.ua, Accessed: Jun. 29, 2022. [Online]. Available: http://dspace.nuft.edu.ua/bitstream/123456789/28927/1/Conf_STRISITT19_20190322.pdf#page=105.

[12] C. Zong, R. Xia, and J. Zhang, Text Data Mining. 2021.

[13] X. Yang, Introduction to algorithms for data mining and machine learning. 2019.

[14] A. S. Osman, “Data mining techniques,” 2019, Accessed: Jun. 29, 2022. [Online]. Available: http://ojs.mediu.edu.my/index.php/IJDSR/article/view/1841.

[15] A. Wang, G. Chen, J. Yang, … S. Z.-I. S., and undefined 2016, “A comparative study on human activity recognition using inertial sensors in a smartphone,” ieeexplore.ieee.org, Accessed: Jun. 29, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7439743/.

[16] W. S. Lima, E. Souto, K. El-Khatib, R. Jalali, J. G.- Sensors, and undefined 2019, “Human activity recognition using inertial sensors in a smartphone: An overview,” mdpi.com, doi: 10.3390/s19143213.

[17] M. Shoaib, … H. S.-2013 I. 10th, and undefined 2013, “Towards physical activity recognition using smartphone sensors,” ieeexplore.ieee.org, 2013, doi: 10.1109/UIC-ATC.2013.43.

[18] Y. J. Rakesh, R. Kavitha, J. J.-I. D. E. and, and undefined 2021, “Human activity recognition using wearable sensors,” Springer, Accessed: Jun. 29, 2022. [Online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-5679-1_51.

[19] F. Li, K. Shirahama, M. A. Nisar, L. Köping, and M. Grzegorzek, “Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors,” Sensors 2018, Vol. 18, Page 679, vol. 18, no. 2, p. 679, Feb. 2018, doi: 10.3390/S18020679.

[20] J. Wannenburg, R. M.-I. T. on Systems, and undefined 2016, “Physical activity recognition from smartphone accelerometer data for user context awareness sensing,” ieeexplore.ieee.org, Accessed: Jun. 29, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7476869/.

[21] C. Zhang and Y. Ma, Ensemble machine learning: methods and applications. 2012.

[22] X. Dong, Z. Yu, W. Cao, Y. Shi, Q. M.-F. of C. Science, and undefined 2020, “A survey on ensemble learning,” Springer, vol. 2020, no. 2, pp. 241–258, Apr. 2020, doi: 10.1007/s11704-019-8208-z.

[23] O. Sagi, L. R.-W. I. R. Data, and undefined 2018, “Ensemble learning: A survey,” Wiley Online Libr., Accessed: Jun. 29, 2022. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1249.

[24] O. Sagi and L. Rokach, “Ensemble learning: A survey,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 8, no. 4, Jul. 2018, doi: 10.1002/WIDM.1249.

[25] N. Hardiyanti, A. Lawi, F. A.-2018 2nd E. Indonesia, and undefined 2018, “Classification of human activity based on sensor accelerometer and gyroscope using ensemble SVM method,” ieeexplore.ieee.org, p. 2018, Accessed: Jun. 15, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8878627/.

[26] F. A.-J. of S. and C. E. (JSCE) and undefined 2021, “Klasifikasi Aktivitas Manusia menggunakan metode Ensemble Stacking berbasis Smartphone,” journal.unpacti.ac.id, vol. 1, no. 2, p. 53, 2021, Accessed: Jun. 15, 2022. [Online]. Available: http://journal.unpacti.ac.id/index.php/JSCE/article/view/171.

[27] F. Aziz, S. Usman, J. J.-J. M. INFORMATIKA, and undefined 2021, “Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost,” stmik-budidarma.ac.id, Accessed: Jun. 15, 2022. [Online]. Available: http://stmik-budidarma.ac.id/ejurnal/index.php/mib/article/view/3222.

[28] F. Aziz, Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope Dan Gravity Menggunakan Algoritma Multi-Class Ensemble Gradientboost. 2021.

[29] I. Faisal, T. Purboyo, A. A.-J. E. A. Sci, and undefined 2019, “A Review of accelerometer sensor and gyroscope sensor in IMU sensors on motion capture,” academia.edu, Accessed: Jun. 29, 2022. [Online]. Available: https://www.academia.edu/download/71039855/826-829.pdf.

[30] A. Javed, M. Sarwar, S. Khan, C. Iwendi, M. M.- Sensors, and undefined 2020, “Analyzing the effectiveness and contribution of each axis of tri-axial accelerometer sensor for accurate activity recognition,” mdpi.com, Accessed: Jun. 29, 2022. [Online]. Available: https://www.mdpi.com/691042.

[31] A. Syafiq, A. Sukor, N. A. Rahim, A. S. Abdull Sukor, A. Zakaria, and N. A. Rahim, “Activity recognition using accelerometer sensor and machine learning classifiers,” ieeexplore.ieee.org, 2018, doi: 10.1109/CSPA.2018.8368718.

[32] R. F. Tinder, “Relativistic flight mechanics and space travel: A primer for students, engineers and scientists,” Synth. Lect. Eng., vol. 1, pp. 1–112, Jan. 2006, doi: 10.2200/S00042ED1V01Y200611ENG001.

[33] G. Milette and A. Stroud, Professional Android sensor programming. 2012.

[34] L. Xie, H. Xian, X. Tang, W. Guo, … F. H.-… C. on P., and undefined 2019, “G-Key: An Authentication Technique for Mobile Devices Based on Gravity Sensors,” ieeexplore.ieee.org, Accessed: Jun. 29, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9017188/.

[35] S. Sattar, S. Li, M. C.- Sensors, and undefined 2018, “Road surface monitoring using smartphone sensors: A review,” mdpi.com, doi: 10.3390/s18113845.

[36] S. Koenig, S. Rombach, … W. G.-… I. S. and, and undefined 2019, “Towards a navigation grade Si-MEMS gyroscope,” ieeexplore.ieee.org, Accessed: Jun. 29, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8943770/.

[37] V. De Jesus, C. Pérez, … A. D. O.-P., and undefined 2018, “Understanding the gyroscope sensor: a quick guide to teaching rotation movements using a smartphone,” iopscience.iop.org, Accessed: Jun. 29, 2022. [Online]. Available: https://iopscience.iop.org/article/10.1088/1361-6552/aae3fc/meta.

[38] E. Boateng, D. A.-J. of data analysis and information, and undefined 2019, “A review of the logistic regression model with emphasis on medical research,” scirp.org, Accessed: Jun. 29, 2022. [Online]. Available: https://www.scirp.org/journal/paperinformation.aspx?paperid=95655.

[39] G. Gasso, “Logistic regression,” 2019, Accessed: Jun. 29, 2022. [Online]. Available: https://moodle.insarouen.fr/pluginfile.php/7984/mod_resource/content/6/Parties_1_et_3_DM/RegLog_Eng.pdf.

[40] E. Bisong, “Logistic Regression,” Build. Mach. Learn. Deep Learn. Model. Google Cloud Platf., pp. 243–250, 2019, doi: 10.1007/978-1-4842-4470-8_20.

[41] F. Aziz, A. Lawi, E. B.-2019 5th I. Conference, and undefined 2019, “Increasing Accuracy of Ensemble Logistics Regression Classifier by Estimating the Newton Raphson Parameter in Credit Scoring,” ieeexplore.ieee.org, doi: 10.1109/CAIPT.2017.8320700.

[42] V. K. Ayyadevara, “Gradient Boosting Machine,” Pro Mach. Learn. Algorithms, pp. 117–134, 2018, doi: 10.1007/978-1-4842-3564-5_6.

[43] J. F.-C. statistics & data analysis and undefined 2002, “Stochastic gradient boosting,” Elsevier, 1999, Accessed: Jun. 29, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167947301000652.

[44] G. Biau, B. Cadre, and L. Rouvière, “Accelerated gradient boosting,” Mach. Learn., vol. 108, no. 6, pp. 971–992, Jun. 2019, doi: 10.1007/S10994-019-05787-1.

[45] J. Thomas, S. Coors, and B. Bischl, “Automatic Gradient Boosting,” Jul. 2018, Accessed: Jun. 29, 2022. [Online]. Available: http://arxiv.org/abs/1807.03873.

[46] F. Provost, R. K.-M. learning, and undefined 1998, “Guest editors’ introduction: On applied research in machine learning,” academia.edu, Accessed: Mar. 08, 2022. [Online]. Available:https://www.academia.edu/download/40088606/Guest_Editors_Introduction_On_Applied_R20151116-31348-ccsgqm.pdf.



DOI: https://doi.org/10.22146/ijccs.73179

Article Metrics

Abstract views : 1414 | views : 1168

Refbacks

  • There are currently no refbacks.




Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2