A New Algorithm to Predict Missing Values in Datasets

Document Type : Computer Article

Authors

kerman

Abstract

Most Datasets related to data mining and machine learning contain data with missing values. How to deal with missing values and to provide solutions based on estimating missing values lead to a very important issue in the field of machine learning and data mining. Among data mining algorithm, the C4.5 algorithm has been used repeatedly because of performance being used in various applications and also ability in working and estimating missing values in data sets. Researchers have presented various methods for deal with missing values and estimating it’s amount in a C4.5 data sets which any of their method causes an increase in accuracy of decision tree and there for produce a more effective and efficient decision. In this paper, for estimating missing values in data sets, at the first, we review the previous methods then the proposed approach as a displacement properties method and in the end the accuracy of proposed methods for deletion and average will be comparing.

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References

 
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Journal of Modeling in Engineering
Volume 16, Issue 55
December 2018
Pages 155-162

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History

  • Receive Date: 14 December 2016
  • Revise Date: 21 January 2018
  • Accept Date: 06 February 2018

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