Browse > Article
http://dx.doi.org/10.6109/jkiice.2020.24.5.600

Pre-processing Method of Raw Data Based on Ontology for Machine Learning  

Hwang, Chi-Gon (Department of Computer Engineering, Institute of Information Technology)
Yoon, Chang-Pyo (Department Of Computer & Mobile Convergence, GyeongGi University of Science and Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.5, 2020 , pp. 600-608 More about this Journal
Abstract
Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.
Keywords
Ontology; Machine Learning; Data Pre-processing; Input Normalization; RSSI;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 L.C.Navarro, A.K.W. Navarro, A.Gregio, A.Rocha, and R.Dahab, "Leveraging ontologies and machine-learning techniques for malware analysis into Android permissions ecosystems," Computers & Security, vol. 78, pp. 429-453, Sep. 2018.   DOI
2 X.Wang, and H.C.Kim, "Text Categorization with Improved Deep Learning Methods," Journal of information and communication convergence engineering, vol. 16, no.2, pp.106-113, 2018.   DOI
3 H. Xiong, G. Pandey, and M. Steinbach, V. Kumar, "Enhancing data analysis with noise removal," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 3, pp.304-319, 2006.   DOI
4 A. H. Doan, J. Madhavan, P. Domingos, and A. Halevy, "Ontology matching: A machine learning approach," In Handbook on Ontologies. Springer, Berlin, Heidelberg. pp. 385-403, 2004.
5 T.R.Gruber, "Towards principles for the design of ontologies used for knowledge sharing," International Journal of Human-Computer Studies, vol. 43, no. 5, pp.1-2, 1995.   DOI
6 C. P. Yoon, and C. G. Hwang, "Efficient indoor positioning systems for indoor location-based service provider," Journal of the Korea Institute of Information and Communication Engineering, vol.19, no.6, pp.1368-1373, 2015.   DOI
7 M. S. Devi, and H. Mittal, "Machine learning techniques with ontology for subjective answer evaluation," International Journal on Natural Language Computing (IJNLC), vol. 5, no. 2, 2016.
8 B. Amann, C. Beeri, I. Fundulaki, and M. Scholl, "Ontology-Based Integration of XML Web Resources," In International Semantic Web Conference (ISWC), pp.117-131, 2002.
9 Architecture Committee, "Semantic Web Services Architecture requirements," Working draft, Version 1.0, [Internet]. Available: http://www.daml.org/services/swsa/swsa-requirements.html, 2004.
10 Y.Bengio, "Learning deep architectures for AI," Foundations and trends in Machine Learning, vol.2, no.1, pp.1-127, 2009.   DOI
11 A.Singh, "Foundations of Machine Learning," Available at SSRN 3399990, 2019.
12 T. Miyato, S. Maeda, M. Koyama, and S. Ishii, "Virtual adversarial training: a regularization method for supervised and semi-supervised learning," IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 8, pp.1979-1993, 2018.   DOI