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Investigation of Lazy Classification in Data Mining using WEKA tool

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  • Paramjeet Kaur
  • Poonam Rani

Abstract

Lazy classification is would allow data domain of complex nature that cannot be properly explained by various learning algorithms. In this research we are getting Correlation coefficient error, mean absolute error, root mean square error, relative absolute error, root relative square error analysis using WEKA. Calculation of mean absolute error, root mean square error, relative absolute error, root relative square error analysis using WEKA would be made for KSTAR, LWL, and IBK. Comparative analysis would be made of all these three lazy classifiers. Here we would take real dataset of advance handsets. In this reading there are mobile name, screen size, CPU speed, number of Sims, Ram size and pixel. It is found from research that there is minimum error in case of KSTAR.

Suggested Citation

  • Paramjeet Kaur & Poonam Rani, 2018. "Investigation of Lazy Classification in Data Mining using WEKA tool," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(3), pages 1613-1617, April.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i3:id:hcseit1833563
    Note: Article URL: https://ijsrcseit.com/CSEIT1833563
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