Abstract
Due to companies’ awareness of the real value of data, the amount of data they handle has increased significantly in recent years [20]. In order to obtain value from the data collected in each company, Big Data and Data Analytics techniques have to be applied. In carrying out this analysis in order to obtain valuable information for the company, several issues related to the computing power of the machines often emerge due to the high volume of data collected. In this paper we emphasis the importance of processing data effectively through data compression and an approach that can help to achieve this. In particular, we have used Bayesian networks to perform data compression without missing useful information.
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Notes
- 1.
DCN: Deep Convolutional Network.
- 2.
Weka: Waikato Environment for Knowledge Analysis is a free software licensed which contains a collection of visualization tools and algorithms for data analysis and predictive modeling.
References
benchmarks.ai: CIFAR-10. https://benchmarks.ai/cifar-10. Accessed 01 Jul 2021
Chen, W., Wilson, J.T., Tyree, S., Weinberger, K.Q., Chen, Y.: Compressing convolutional neural networks. arXiv preprint arXiv:1506.04449 (2015)
Cutler, A., Cutler, D.R., Stevens, J.R.: Random Forests. In: Zhang, C., Ma, Y. (eds.) Ensemble Machine Learning. Springer, Boston (2012). https://doi.org/10.1007/978-1-4419-9326-7_5
EduardoMagalhãesOliveira: Quality Prediction in a Mining Process. https://www.kaggle.com/edumagalhaes/quality-prediction-in-a-mining-process. Accessed 15 Mar 2020
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)
Gopalkrishnan, V., Steier, D., Lewis, H., Guszcza, J.: Big data, big business: bridging the gap. In: Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, pp. 7–11 (2012)
Han, S., et al.: EIE: efficient inference engine on compressed deep neural network. In: 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pp. 243–254. IEEE (2016)
Kaur, G., Oberai, E.N.: A review article on Naive Bayes classifier with various smoothing techniques. Int. J. Comput. Sci. Mob. Comput. 3(10), 864–868 (2014)
Kozma, L.: k nearest neighbors algorithm (KNN). Helsinki University of Technology (2008)
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014). https://doi.org/10.1007/s12599-014-0334-4
LeCun, Y., Denker, J.S., Solla, S.A.: Optimal brain damage. In: Advances in Neural Information Processing Systems, pp. 598–605 (1990)
Lerner, B., Malka, R.: Investigation of the k2 algorithm in learning Bayesian network classifiers. Appl. Artif. Intell. 25(1), 74–96 (2011)
Leung, K.M.: Naive Bayesian Classifier, pp. 123–156. Department of Computer Science/Finance and Risk Engineering, Polytechnic University (2007)
Lin, D., Talathi, S., Annapureddy, S.: Fixed point quantization of deep convolutional networks. In: International Conference on Machine Learning, pp. 2849–2858 (2016)
Louizos, C., Ullrich, K., Welling, M.: Bayesian compression for deep learning. In: Advances in Neural Information Processing Systems, pp. 3288–3298 (2017)
Manzanares, E.M.: Hill-Climbing. https://ccc.inaoep.mx/~emorales/Cursos/Busqueda/node23.html. Accessed 20 Mar 2020
Mathuria, M.: Decision tree analysis on j48 algorithm for data mining. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(6) (2013)
Nalisnick, E., Anandkumar, A., Smyth, P.: A scale mixture perspective of multiplicative noise in neural networks. arXiv preprint arXiv:1506.03208 (2015)
Nick, T.G., Campbell, K.M.: Logistic regression. In: Topics in Biostatistics, pp. 273–301 (2007)
redaction, C.: El volumen de datos en las empresas crece un 569% en dos años. https://www.computing.es/analytics/noticias/1113253046201/volumen-de-datos-empresas-crece-569-dos-anos.1.html. Accessed 22 Mar 2020
Sucar, L.: Clasificadores bayesianos: de datos a conceptos. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2008 (2008)
Thames, L., Schaefer, D.: Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)
Wilson, D.R., Martinez, T.R.: Instance pruning techniques. In: ICML 1997, pp. 400–411 (1997)
Zheng, F., Webb, G.I.: Tree Augmented Naive Bayes, pp. 990–991. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8_850
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Larrakoetxea, N.G., Urquijo, B.S., López, I.P., Barruetabeña, J.G., Bringas, P.G. (2022). Optimizing Communication Data Streams in Edge Computing Systems Using Bayesian Algorithms. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_12
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