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A Comparison of Five Methods for Missing Value Imputation in Data Sets
| Content Provider | Semantic Scholar |
|---|---|
| Author | Cihan, Pınar |
| Copyright Year | 2018 |
| 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 (RMSE) value, classification accuracy and execution time are used to evaluate the performances of most popular five methods (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. |
| Starting Page | 80 |
| Ending Page | 85 |
| Page Count | 6 |
| File Format | PDF HTM / HTML |
| Volume Number | 2 |
| Alternate Webpage(s) | https://dergipark.org.tr/download/article-file/614943 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
