DOI QR코드

DOI QR Code

A Study on Accuracy Estimation of Service Model by Cross-validation and Pattern Matching

  • Cho, Seongsoo ;
  • Shrestha, Bhanu
  • Received : 2017.06.16
  • Accepted : 2017.07.15
  • Published : 2017.09.30

Abstract

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

Keywords

Machine learning method;Pattern matching;Cross validation

References

  1. Calero, J. M. A., Ortega, A. M., Perez, G. M., Botia, J. A., and Gomez-Skarmeta, A. F. (2012). A Non-monotonic Expressiveness Extension on the Semantic Web Rule Language. J. Web Eng., 11(2), 93-118.
  2. Skillen, K. L., Chen, L., Nugent, C. D., Donnelly, M. P., Burns, W., and Solheim, I. (2014). Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments. Future Generation Computer Systems, 34, 97-109. https://doi.org/10.1016/j.future.2013.10.027
  3. Hoque, M. R., Kabir, M. H., Seo, H., and Yang, S. H. (2016). PARE: Profile-Applied Reasoning Engine for Context-Aware System. International Journal of Distributed Sensor Networks, 12(7), 5389091.
  4. Nimbalkar, D. D., and Shah, P. (2013). Data mining using RFM Analysis. International Journal of Scientific & Engineering Research (IJSRE), 4(12), 940-943.
  5. Essa, I. A. (1999, July). Ubiquitous sensing for smart and aware environments: technologies towards the building of an aware home. In Position paper for the DARPA/NSF/NIST Workshop on Smart Environments.
  6. Bengio, Y., and Grandvalet, Y. (2004). No unbiased estimator of the variance of k-fold cross-validation. Journal of machine learning research, 5(Sep), 1089-1105.
  7. Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010). Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE transactions on pattern analysis and machine intelligence, 32(3), 569-575. https://doi.org/10.1109/TPAMI.2009.187
  8. Mozer, M. C. (1998, March). The neural network house: An environment hat adapts to its inhabitants. In Proc. AAAI Spring Symp. Intelligent Environments (Vol. 58).
  9. L. Rokach and O. Maimon, "Data miningwith Decision Trees : Theoy and Application",World Scientific, Oct., 2007.
  10. Patnaik, S. (2007). Robot cognition and navigation: an experiment with mobile robots. Springer Science & Business Media.