Advertisement

Supervised Machine Learning Techniques in Intelligent Network Handovers

  • Anandakumar Haldorai
  • Umamaheswari Kandaswamy
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Supervised Machine Learning (SML) is a critical analysis of algorithms, which conform to exterior abounding instances that determine the overall hypotheses and future instance predictions. In intelligent systems, supervised categorization is a critical aspect of machine learning. This article comprehends on fundamental SML techniques, through the comparison of different SML algorithms and determination of the most crucial supervised classification algorithm. These techniques: Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), JRip, Neural Networks, and the Decision tree, through the application of the Waikato Environmental of Knowledge Networks (WEKA) as a machine learning application. Considering the implementation of algorithm, the dataset on Diabetes was utilized during the classification process considering 789 instances composing of 8 attributes as a dependent variable and another as an independent variable in the analysis. Considering the discussion and the results, it was evident that SVM is precise and accurate, termed as an algorithm (Zhang N, Int. J. Collab. Intell. 1:298, 2016). The Random Forest and Naïve Bayes categorizing algorithm was denoted to be precise subsequent to the SVM. This paper indicates that the timeframe utilized to formulate precision and model is a different factor from the mean absolute error and the kappa statistic. Resultantly, the machine learning algorithm necessitate accuracy, precision, and minimal error in obtaining predictive SML.

Keywords

Supervised Machine Learning (SML) Data Mining (DM) Data Analysis (DA) Learning Algorithms (LA) Classifiers 

References

  1. 1.
    Zhang, N.: Semi-supervised extreme learning machine with wavelet kernel. Int. J. Collab. Intell. 1(4), 298 (2016)Google Scholar
  2. 2.
    Praveena, M., Jaiganesh, V.: A literature review on supervised machine learning algorithms and boosting process. Int. J. Comput. Appl. 169(8), 32–35 (2017)Google Scholar
  3. 3.
    Bostik, O., Klecka, J.: Recognition of CAPTCHA characters by supervised machine learning algorithms. IFAC-PapersOnLine. 51(6), 208–213 (2018)CrossRefGoogle Scholar
  4. 4.
    Drotár, P., Smékal, Z.: Comparative study of machine learning techniques for supervised classification of biomedical data. Acta Electrotech. Inform. 14(3), 5–10 (2014)CrossRefGoogle Scholar
  5. 5.
    Suganya, M., Anandakumar, H.: Handover based spectrum allocation in cognitive radio networks. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 215–219 (2013)Google Scholar
  6. 6.
    Anandakumar, H., Umamaheswari, K.: Energy efficient network selection using 802.16g based GSM technology. J. Comput. Sci. 10(5), 745–754 (2014)CrossRefGoogle Scholar
  7. 7.
    Matuszyk, P., Spiliopoulou, M.: Stream-based semi-supervised learning for recommender systems. Mach. Learn. 106(6), 771–798 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Belkin, M., Niyogi, P.: Semi-supervised learning on riemannian manifolds. Mach. Learn. 56(1–3), 209–239 (2004)CrossRefGoogle Scholar
  9. 9.
    Sarkar, S., Soundararajan, P.: Supervised learning of large perceptual organization: graph spectral partitioning and learning automata. IEEE Trans. Pattern Anal. Mach. Intell. 22(5), 504–525 (2000)CrossRefGoogle Scholar
  10. 10.
    Ma, J., Wen, Y., Yang, L.: Lagrangian supervised and semi-supervised extreme learning machine. Appl. Intell. 49(2), 303–318 (2019)CrossRefGoogle Scholar
  11. 11.
    Chen, K., Shihai, W.: Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 129–143 (2011)CrossRefGoogle Scholar
  12. 12.
    Huang, R., Zhou, P., Zhang, L.: A LDA-based approach for semi-supervised document clustering. Int. J. Mach. Learn. Comput. 4(4), 313–318 (2014)CrossRefGoogle Scholar
  13. 13.
    Subramanya, A., Talukdar, P.: Graph-based semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 8(4), 1–125 (2014)CrossRefGoogle Scholar
  14. 14.
    Krogel, M., Scheffer, T.: Multi-relational learning, text mining, and semi-supervised learning for functional genomics. Mach. Learn. 57(12), 61–81 (2004)CrossRefGoogle Scholar
  15. 15.
    Iosifidis, A.: Extreme learning machine based supervised subspace learning. Neurocomputing. 167, 158–164 (2015)CrossRefGoogle Scholar
  16. 16.
    Nishii, R.: Supervised image classification based on statistical machine learning. SPIE Newsroom. (2007)Google Scholar
  17. 17.
    Sądel, B., Śnieżyński, B.: Online supervised learning approach for machine scheduling. Schedae Informaticae. 25, 165–176 (2017)Google Scholar
  18. 18.
    Anandakumar, H., Umamaheswari, K.: An efficient optimized handover in cognitive radio networks using cooperative spectrum sensing. Intell. Autom. Soft Comput. 1–8 (2017)Google Scholar
  19. 19.
    Anandakumar, H., Arulmurugan, R., C. C. Onn.: Computational intelligence and sustainable systems. In: EAI/Springer Innovations in Communication and Computing (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anandakumar Haldorai
    • 1
  • Umamaheswari Kandaswamy
    • 2
  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Information TechnologyPSG College of TechnologyCoimbatoreIndia

Personalised recommendations