A cyclic attribution technique feature selection method for human activity recognition

A cyclic attribution technique feature selection method for human activity recognition

Автор: Win Win Myo, Wiphada Wettayaprasit, Pattara Aiyarak

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 10 vol.11, 2019 года.

Бесплатный доступ

Feature selection is a technique of selecting the most important features for predictive model construction. It is a key component in machine learning for many pattern recognition applications. The primary objective of this paper is to create a more precise system for Human Activity Recognition (HAR) by identifying the most appropriate features. We propose a Cyclic Attribution Technique (CAT) feature selection technique for recognition of human activity based on group theory and the fundamental properties of the cyclic group. We tested our model on UCI-HAR dataset focusing on six activities. With the proposed method, 561 features could be reduced to 63. Using an Artificial Neural Network (ANN), we compared performances of our new dataset with selected features and the original dataset classifier. Results showed that the model could provide an excellent overall accuracy of 96.7%. The proposed CAT technique can specify high-quality features to the success of HAR with ANN classifier. Two benefits support this technique by reducing classification overfitting and training time.

Еще

Feature selection, Attribution technique, Human activity recognition, Cyclic group, Artificial Neural Network

Короткий адрес: https://sciup.org/15016627

IDR: 15016627   |   DOI: 10.5815/ijisa.2019.10.03

Список литературы A cyclic attribution technique feature selection method for human activity recognition

  • G. Forman, “An Extensive Empirical Study of Feature Selection Metrics for Text Classification,” Journal of machine learning research, vol. 3, pp. 1289–1305, 2003.
  • I. Guyon, “An Introduction to Variable and Feature Selection 1 Introduction,” Journal of machine learning research., vol. 3, pp. 1157–1182, 2003.
  • U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher, “Activity recognition and monitoring using multiple sensors on different body positions,” Proceedings-BSN 2006: International Workshop on Wearable and Implantable Body Sensor Networks, vol. 2006, no. May 2006, pp. 113–116, 2006.
  • M. Arif, M. Bilal, and A. Kattan, “Better Physical Activity Classification using Smartphone Acceleration Sensor,” Journal of Medical Systems no. September, pp. 0–10, 2014.
  • I. Suarez, A. Jahn, C. Anderson, and K. David, “Improved Activity Recognition by Using Enriched Acceleration Data,” Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'15)., pp. 1011–1015, 2015.
  • L. Zhen and L. Qiong, “A New Feature Selection Method for Internet Traffic Classification Using ML,” Physics Procedia International Conference on Medical Physics and Biomedical Engineering., vol. 33, no. Ml, pp. 1338–1345, 2012.
  • W. WinMyo, P. Aiyarak, and W. Wettayaprasit, “A Noble Feature Selection Method for Human Activity Recognition using Linearly Dependent Concept ( LDC ),” in the 7th International Conference on Software and Computer Applications, 2018, no. Ldc, pp. 173–177.
  • D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A Public Domain Dataset for Human Activity Recognition Using Smartphones,” European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning., no. April, pp. 24–26, 2013.
  • L. Wang, “Recognition of Human Activities with Wearable Sensors,” EURASIP Journal on Advances in Signal Processing., vol. 2012, no. 108, pp. 1–13, 2012.
  • Z. Zhang, J. Dong, X. Luo, K. S. Choi, and X. Wu, “Heartbeat classification using disease-specific feature selection,” Computers in Biology and Medicine.., vol. 46, no. 1, pp. 79–89, 2014.
  • Y. Guo et al., “Tensor Manifold Discriminant Projections for Acceleration-Based Human Activity Recognition,” IEEE Transactions on Multimedia., vol. 18, no. 10, pp. 1977–1987, 2016.
  • J. Derrac, S. Garc??a, and F. Herrera, “IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule,” Pattern Recognittion., vol. 43, no. 6, pp. 2082–2105, 2010.
  • M. Simão, P. Neto, and O. Gibaru, “Using data dimensionality reduction for recognition of incomplete dynamic gestures,” Pattern Recognition Letters., vol. 0, pp. 1–7, 2017.
  • J. Kersten, “Simultaneous feature selection and Gaussian mixture model estimation for supervised classification problems,” Pattern Recognition., vol. 47, no. 8, pp. 2582–2595, 2014.
  • J. Howcroft, J. Kofman, and E. D. Lemaire, “Feature selection for elderly faller classification based on wearable sensors,” Journal of Neuro-Engineering and Rehabilitation., vol. 14, no. 1, pp. 1–11, 2017.
  • W. W. Myo, W. Wettayaprasit, and P. Aiyarak, “Designing Classifier for Human Activity Recognition Using Artificial Neural Network,” 2019 4th International Conferrence on IEEE Computer. Communijcation. System, pp. 81–85, 2019.
  • L. Cao, Y. Wang, B. Zhang, Q. Jin, and A. V. Vasilakos, “GCHAR: An efficient Group-based Context-aware human activity recognition on smartphone,” Journal of Parallel and Distributed Computing., 2016.
  • M. M. Hassan, M. Z. Uddin, A. Mohamed, and A. Almogren, “A robust human activity recognition system using smartphone sensors and deep learning,” Future Generation Computer Systems., vol. 81, pp. 307–313, 2018.
  • D. Acharjee, S. P. Maity, and A. Mukherjee, “Hidden Markov model a tool for recognition of human contexts using sensors of smart mobile phone,” Microsystem Technologies., no. August, pp. 1–12, 2016.
  • D. Acharjee, A. Mukherjee, J. K. Mandal, and N. Mukherjee, “Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors,” Microsystem Technologies., vol. 22, no. 11, pp. 2715–2722, Nov. 2016.
  • J. a Gallian, J. G. Rainbolt, and B. Cole, Abstract Algebra with GAP for Contemporary Abstract Algebra, 2010.
  • M. Eie and S.-T. Chang, “Cyclic Groups,” A Course Abstr. Algebr., pp. 53–64, 2012.
  • A. Cyclic and G. Structures, “As Cyclic Group Structures,” no. M, 2014.
  • S. Spitzer, “Using Star Polygons to Understand Cyclic Group Structure,” pp. 479–480, 2012.
  • M. M. El-farrah, “Expectation Numbers of Cyclic Groups,” 2015.
  • H. Aktaş and Ş. Özlü, “Cyclic soft groups and their applications on groups,” Scientific World Journal., vol. 2014, 2014.
  • G. Oman and V. Slattum, “A Characterization of the Cyclic Groups by Subgroup Indices,” Coll. Math. J., vol. 47, no. 1, pp. 29–33, 2016.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp