Rough Sets Computations to Impute Missing Data
| dc.creator | Nelwamondo, Fulufhelo Vincent | |
| dc.creator | Marwala, Tshilidzi | |
| dc.date | 2007-04-26 | |
| dc.date.accessioned | 2026-07-07T07:58:32Z | |
| dc.date.available | 2026-07-07T07:58:32Z | |
| dc.description | Many techniques for handling missing data have been proposed in the literature. Most of these techniques are overly complex. This paper explores an imputation technique based on rough set computations. In this paper, characteristic relations are introduced to describe incompletely specified decision tables.It is shown that the basic rough set idea of lower and upper approximations for incompletely specified decision tables may be defined in a variety of different ways. Empirical results obtained using real data are given and they provide a valuable and promising insight to the problem of missing data. Missing data were predicted with an accuracy of up to 99%. | |
| dc.description | 19 pages | |
| dc.identifier | https://arxiv.org/abs/0704.3635 | |
| dc.identifier | http://arxiv.org/abs/0704.3635 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/128038 | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.subject | Information Retrieval | |
| dc.title | Rough Sets Computations to Impute Missing Data | |
| dc.type | text |