Multiple Activity recognition through combining classifier

Ruchi Pandey*, Shahana Qureshi**
* Department of Computer Science and Engineering, RITEE, Raipur, Chhattisgarh India.
** Professor, Department of Computer Science and Engineering, RITEE, Raipur, Chhattisgarh, India.
Periodicity:March - May'2018
DOI : https://doi.org/10.26634/jpr.5.1.14232

Abstract

The Internet of Things (IOT) is a prominent research area that provides many interesting solutions to various problems experience by various departments. Smart homes applications is one such branch that evolve from IOT with the huge challenges of data storage and handling. Activity recognition is the major challenge in smart homes application that consolidates multiclass learning. The effectiveness of ensemble learner in handling multiclass problem and collective dissicion delivered prompt its uses in the smart homes application. In this paper we deal with activities recognition problems on various ensemble learners including bagging, boosting and random forest. The standard van Kasteren dataset contains three housedata with eight activities of different days. We perform our experiment on the preprocessed collected data and applied six learners i.e. three from individual learner and three from ensemble learners. On performing the extensive experiment it was found that the group of ensemble learner outcast the simple learners.

Keywords

Internet of Things( IOT), Ensemble Learner, Bagging, Boosting, Random Forest.

How to Cite this Article?

Pandey,R., and Qureshi, S. (2018). Multiple Activity Recognition Through Combining Classifier. i-manager’s Journal on Pattern Recognition, 5(1), 1-9. https://doi.org/10.26634/jpr.5.1.14232

References

[1]. Abowd, G. D., Bobick, A. F., Essa, I. A., Mynatt, E. D., & Rogers, W. A. (2002, July). The aware home: A living laboratory for technologies for successful aging. In Proceedings of the AAAI-02 Workshop Automation as Caregiver (pp. 1-7).
[2]. Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2), 105- 139.
[3]. Bifet, A., Holmes, G., Pfahringer, B., & Gavalda, R. (2009, November). Improving adaptive bagging methods for evolving data streams. In Asian Conference on Machine Learning (pp. 23-37). Springer, Berlin, Heidelberg.
[4]. Chen, L., Hoey, J., Nugent, C. D., Cook, D. J., & Yu, Z. (2012). Sensor-based activity recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6), 790-808.
[5]. Cook, D. J., Krishnan, N. C., & Rashidi, P. (2013). Activity discovery and activity recognition: A new partnership. IEEE Transactions on Cybernetics, 43(3), 820- 828.
[6]. Cook, D., & Das, S. K. (2004). Smart Environments: Technology, Protocols and Applications (Vol. 43). John Wiley & Sons.
[7]. Fischer, B., & Buhmann, J. M. (2003). Bagging for pathbased clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(11), 1411-1415.
[8]. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
[9]. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2016). New Ordering-Based pruning metrics for ensembles of Classifiers in Imbalanced th Datasets. In Proceedings of the 9 International Conference on Computer Recognition Systems CORES 2015 (pp. 3-15). Springer, Cham.
[10]. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., & Herrera, F. (2017). NMC: nearest matrix classification - A new combination model for pruning One-vs-One ensembles by transforming the aggregation problem. Information Fusion, 36, 26-51.
[11]. Gardner, S. R.(1998). Building Data warehouse. ACM Digital Library, 41(9), 52-60.
[12]. Lee, T. H., & Yang, Y. (2006). Bagging binary and quantile predictors for time series. Journal of Econometrics, 135(1-2), 465-497.
[13]. MacKay, D. J., & Mac Kay, D. J. (2003). Information Theory, Inference and Learning Algorithms. Cambridge University Press.
[14]. Maurer, U., Smailagic, A., Siewiorek, D. P., & Deisher, M. (2006, April). Activity recognition and monitoring using multiple sensors on different body positions. In Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on (pp. 4-pp). IEEE.
[15]. Modayil, J., Bai, T., & Kautz, H. (2008, September). Improving the recognition of interleaved activities. In th Proceedings of the 10 International Conference on Ubiquitous computing (pp. 40-43). ACM.
[16]. Rifkin, R., & Klautau, A. (2004). In defense of one-vsall classification. Journal of Machine Learning Research, 5(Jan), 101-141.
[17]. Skurichina, M., & Duin, R. P. (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications, 5(2), 121-135.
[18]. Tapia, E. M., Intille, S. S., & Larson, K. (2004, April). Activity recognition in the home using simple and ubiquitous sensors. In International Conference on Pervasive Computing (pp. 158-175). Springer, Berlin, Heidelberg.
[19]. van Kasteren, T. L. M., Englebienne, G., & Kröse, B. J. (2010). Activity recognition using semi-markov models on real world smart home datasets. Journal of Ambient Intelligence and Smart Environments, 2(3), 311-325.
[20]. van Kasteren, T., Noulas, A., Englebienne, G., & Kröse, B. (2008, September). Accurate activity th recognition in a home setting. In Proceedings of the 10 International Conference on Ubiquitous Computing (pp. 1-9). ACM.
[21]. Xiang, J., Weng, J. G., Zhuang, Y. T., & Wu, F. (2006). Ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction. Journal of Zhejiang University-Science A, 7(12), 2063-2072.
[22]. Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., & Tröster, G. (2008). Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In Wireless Sensor Networks (pp. 17-33). Springer, Berlin, Heidelberg.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.