Name
Abstract | ||
|---|---|---|
We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill them in, there is currently no principled way to rank the algorithms using a quantitative metric. We develop a framework based on treating imputation evaluation as a problem of comparing two distributions and show how it can be used to compute quantitative metrics. We present an efficient procedure for applying this framework to practical datasets, demonstrate several metrics derived from the existing literature on comparing distributions, and propose a new metric called Neighborhood-based Dissimilarity Score which is fast to compute and provides similar results. Results are shown on several datasets, metrics, and imputations algorithms. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Data mining,Algorithm,Artificial intelligence,Missing data,Imputation (statistics),Missing value imputation,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1311.2276 | 0 |
PageRank | References | Authors |
0.34 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vinod Nair | 1 | 1658 | 134.40 |
| Rahul Kidambi | 2 | 32 | 6.75 |
| Sundararajan Sellamanickam | 3 | 127 | 14.07 |
| S. Sathiya Keerthi | 4 | 4455 | 527.30 |
| Johannes Gehrke | 5 | 13362 | 1055.06 |
| Vijay K. Narayanan | 6 | 138 | 10.99 |