A New Method for Missing Value Imputation in Incomplete Information Systems Using Hybrid Rough Set Theory

Authors

  • G. V. Suresh  Research Scholar, Jawaharlal Nehru Technological University, Hyderabad, Telangana, India
  • Dr. E. Sreenivasa Reddy  Professor, University College of Engineering, Acharya Nagarjuna University, Andhra Pradesh, India

Keywords:

Decision Making, Incomplete Information Systems, Rough Set Theory (RST), Missing Value Imputation.

Abstract

Decision making has become a main reason for data analysis in the current scenario. Before analysis, the data must be freed from noise by applying data pre-processing techniques to the raw data. Missing value imputation is one of the data cleaning methods in data preprocessing. This article presents a new data imputation technique with the concepts of approximate set theory. A Hybrid Rough Set Theory for Missing Value Imputation (HRST-MVI) imputation algorithm is developed. The performance of the proposed algorithm is carried out by comparing the classification accuracy obtained, after the imputation of the missing value. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1 score. The performance of the classifiers shows that the HRST-MVI can impute missing values from multiple patterns more efficiently than other comparative methods.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-08-31

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
G. V. Suresh, Dr. E. Sreenivasa Reddy, " A New Method for Missing Value Imputation in Incomplete Information Systems Using Hybrid Rough Set Theory" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.580-592, July-August-2020.