A Stacking Model for Outlier Prediction using Learning Approaches
Keywords:
Outliers, Deep Learning, Auto-Encoder, CNN, StackingAbstract
Outliers are considered as unexpected things observed while analyzing the data. Investigators found that the prediction and identification of outliers are extremely complex. Generally, a stream is measured as an unbounded data source executed promptly, and this research provides a novel way of predicting the outliers over the incoming data. Here, the incoming data is acquired from the hospital to validate the patients' records. There are higher chances of outliers over the incoming data based on the density of the arrival data. The execution of outlier prediction is performed independently with the integration of LSTM (Long Short Term Memory) over the stacked CNN model. The outlier detection process constantly measures the incoming data from the emotional input as an outlier or inlier. Here, the data reconstruction is achieved with the auto-Regressive model, and the prediction model considers the outliers to construct the training data. Various hidden representation acquired from the stacked model is considered for outlier prediction, and the experimental results demonstrate that the anticipated model shows superior prediction accuracy, specificity, sensitivity and F1 score. The proposed model is best-suited for prediction as the deep learning approaches perform well over complex applications and acquire superior results. The simulation is done in MATLAB 2016b, and the performance metrics show a better trade-off than the other approaches.
Downloads
References
Aggarwal, C., Zhao, Y., Yu, P.: Outlier detection in graph streams. In: Proceedings of the 27th International Conference on Data Engineering, pp. 399–409 (2011)
Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining, SIAM, pp. 90–98 (2017)
De Stefani, J., Le Borgne, Y.A., Caelen, O., Hattab, D., Bontempi, G.: Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. Int. J. Data Sci. Anal. (2018).
Ferdowsi, H., Jagannathan, S., Zawodniok, M.: An online outlier identification and removal scheme for improving fault detection performance. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 908– 919 (2014)
File to, R., May, C., Renso, C., Pelekis, N., Klein, D., Theodoridis, Y.: The Baquara2 knowledge-based framework for semantic enrichment and analysis of movement data. Data Knowl. Eng. 98, 104–122 (2015)
Chandarana, D. R., and M. V. Dhamecha 2015. A survey for different approaches to Outlier Detection in data mining. International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). Visakhapatnam, India
Guo, F., C. Shi, X. Li, J. He, and W. Xi 2018. Outlier detection is based on the data structure. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, IEEE, 1–6.
Jafarizadeh, V., A. Keshavarzi, and T. Derikvand. 2017. Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks. Wireless Networks 23 (3):779–85. doi:10.1007/s11276-015-1169-8
Kiani, R., S. Mahdavi, and A. Keshavarzi. 2015. Analysis and prediction of crimes by clustering and classification. International Journal of Advanced Research in Artificial Intelligence 4 (8):11–17. doi:10.14569/ISSN.2165-4069
Ko, T., J. H. Lee, H. Cho, S. Cho, W. Lee, and M. Lee. 2017. Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data. Industrial Management & Data Systems 117:927–45.
Lin, C., Q. Zhu, S. Guo, Z. Jin, Y. R. Lin, and N. Cao. 2018. Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis. Data Mining and Knowledge Discovery 32 (4):1056–73.
Riahi, F., and O. Schulte 2015. Model-based outlier detection for object-relational data. Computational Intelligence, 2015 IEEE Symposium Series, Cape Town, South Africa, IEEE, 1590–98.
Zhang, J., J. Cao, and X. Zhu 2012. Detecting global outliers from large distributed databases. Proceedings of the 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012), Sichuan, China, IEEE, 1632–36
Zhao, Y., and M. K. Hryniewicki 2018. Xgbod: Improving supervised outlier detection with unsupervised representation learning. 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, IEEE, 1–8.
Zhang, Z., J. Li, C. Manikopoulos, J. Jorgenson, and J. Ucles 2001. HIDE: A hierarchical network intrusion detection system using statistical pre-processing and neural network classification. Proc. IEEE Workshop on Information Assurance and Security, 85–90.
Zhang, L., C. Liu, Y. Chen, and S. Lao 2018. Abnormal Detection Research Based on Outlier Mining. 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA), Changsha, China, IEEE, 5–7.
Yang, S., and W. Liu 2011. Anomaly detection on collective moving patterns: A hidden Markov model-based solution. 2011 IEEE International Conferences on Internet of Things, Cyber, Physical and Social Computing, Dalian, China, 291–96.
Vatanen, T., M. Kuusela, E. Malmi, T. Raiko, T. Aaltonen, and Y. Nagai 2012. Semisupervised detection of collective anomalies with an application in high-energy particle physics. IJCNN, Brisbane, QLD, Australia, 1–8.
Rasheed, F., Alhajj, R.: A framework for periodic outlier pattern detection in time-series sequences. IEEE Trans. Cybern. 44(5), 569–582 (2014)
Ma, Y., Zhang, P., Cao, Y., Guo, L.: Parallel auto-encoder for efficient outlier detection. In: Proceedings of the 2013 IEEE International Conference on Big Data, pp. 15–17 (2013)
Tran, L., Fan, L., Shahabi, C.: Distance-based outlier detection in data streams. Proc. VLDB Endow. 9(12), 1089–1100 (2016)
Xu, H., Wang, Y., Cheng, L., Wang, Y., Ma, X.: Exploring a high-quality outlying feature value set for noise-resilient outlier detection in categorical data. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, ACM, New York, NY, USA, CIKM ’18, pp. 17–26. (2018).
Zhang, H., Nian, K., Coleman, T.F., Li, Y.: Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection. Int. J. Data Sci. Anal. (2018).
Wang, G., Hao, J., Ma, J., Huang, L.: A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37(9), 6225–6232 (2010)
Söelch, M., Bayer, J., Ludersdorfer, M., van der Smagt, P.: Variational inference for on-line anomaly detection in high-dimensional time series (2016). arXiv preprint arXiv:1602.07109
Miyaguchi, K., Yamanishi, K.: Online detection of continuous changes in stochastic processes. Int. J. Data Sci. Anal. 3(3), 213– 229 (2017)
Rayana, S., Akoglu, L.: Less is more: building selective anomaly ensembles. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 42 (2016)
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. Proc. Eur. Symp. Artif. Neural Netw. 23, 89–94 (2015)
Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain periodic data streams. ACM Trans. Internet Technol. 16(1), 1–20 (2016)
Chauhan, S., Vig, L.: Anomaly detection in ECG time signals via deep extended short-term memory networks. In: Proceedings of IEEE International Conference on Data Science and Advanced Analytics, pp. 1–7 (2015)
Kumar, L. R. ., Ashokkumar, C. ., Pandey, P. S. ., Kannaiah, S. K. ., J, B. ., & Hussan, M. I. T. . (2023). Security Enhancement in Surveillance Cloud Using Machine Learning Techniques. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 46–55. https://doi.org/10.17762/ijritcc.v11i3s.6154
Verma, D. ., Reddy, A. ., & Thota, D. S. . (2021). Fungal and Bacteria Disease Detection Using Feature Extraction with Classification Based on Deep Learning Architectures. Research Journal of Computer Systems and Engineering, 2(2), 27:32. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/29
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
