Name
Abstract | ||
|---|---|---|
In data analysis problems where the data are represented by vectors of real numbers, it is often the case that some of the data-points will have ''missing values'', meaning that one or more of the entries of the vector that describes the data-point is not observed. In this paper, we propose a new approach to the imputation of missing binary values. The technique we introduce employs a ''similarity measure'' introduced by Anthony and Hammer (2006) [1]. We compare experimentally the performance of our technique with ones based on the usual Hamming distance measure and multiple imputation. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.dam.2011.01.024 | ISAIM |
Keywords | DocType | Volume |
data analysis problem,boolean similarity measure,new imputation method,real number,usual hamming distance measure,missing value,missing binary value,similarity measure,imputation,new approach,multiple imputation,incomplete binary data,data analysis,missing values,hamming distance | Conference | 159 |
Issue | ISSN | Citations |
10 | Discrete Applied Mathematics | 4 |
PageRank | References | Authors |
0.52 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Munevver Mine Subasi | 1 | 19 | 4.24 |
| Ersoy Subasi | 2 | 7 | 3.40 |
| Martin Anthony | 3 | 329 | 141.99 |
| Peter L. Hammer | 4 | 1996 | 288.93 |