A Comparison of Five Methods for Missing Value Imputation in Data Sets

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

International Scientific and Vocational Studies Journal
Volume:2 Issue:2
A Comparison of Five Methods for Missing Value Imputation in Data Sets

A Comparison of Five Methods for Missing Value Imputation in Data Sets

Authors : Pınar Cihan

Pages : 80-85

View : 102 | Download : 10

Publication Date : 2018-12-31

Article Type : Research Paper

Abstract :The missing values in the data sets do not allow for accurate analysis. Therefore, the correct imputation of missing values has become the focus of attention of researchers in recent years. This paper focuses on a comparison of most reliable and up to date estimation methods to imputing the missing values. Imputation of missing values has a very high priority because of its impact on next pre-processing, data analysis, classification, clustering, etc. Root mean square error insert ignore into journalissuearticles values(RMSE); value, classification accuracy and execution time are used to evaluate the performances of most popular five methods insert ignore into journalissuearticles values(mean, k-nearest neighbors, singular value decomposition, bayesian principal component analysis and missForest);. When RMSE and classification accuracy values of methods were compared, it has observed that missForest method outperformed other methods in all datasets.
Keywords : Missing value imputation, k nearest neighbor, singular value decomposition, bayesian principal component analysis, missForest

ORIGINAL ARTICLE URL

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.