Research Article Open Access

Statistical Analysis and Learning Method on Users' Feedbacks

Doris Hooi-Ten Wong and Sureswaran Ramadass

Abstract

Problem statement: The purpose of this study was constructing an effective algorithm in order to learn the users’ feedbacks from their displayed visualization. This is due to existing visualization tools typically involve presenting network data regardless of considering level of network data knowledge among different levels of computer users. The machine learning algorithm has been applied in order to find the most effective statistical analysis and learning algorithm in learning users’ feedbacks. Approach: The objectives of this study were to conduct statistical analysis and learning algorithm model for different levels of computer users’ feedbacks and procedure to test the classifier. Results: WEKA the machine learning workbench that supports many activities of machine learning practitioners will be used to implement the proposed algorithm. The implemented program will work as training testing model. Conclusion: We can produce an adaptive visualization to the different levels of computer users as we have learnt their feedbacks (behavior) and update the classifier model.

Journal of Computer Science
Volume 7 No. 9, 2011, 1423-1425

DOI: https://doi.org/10.3844/jcssp.2011.1423.1425

Submitted On: 20 June 2011 Published On: 26 July 2011

How to Cite: Wong, D. H. & Ramadass, S. (2011). Statistical Analysis and Learning Method on Users' Feedbacks. Journal of Computer Science, 7(9), 1423-1425. https://doi.org/10.3844/jcssp.2011.1423.1425

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Keywords

  • Statistical analysis
  • learning method
  • user feedback
  • machine learning
  • effective algorithm
  • displayed visualization
  • existing visualization
  • network data
  • different levels of computer
  • learning algorithm