Reduct based decision tree (RDT)
Reduct based decision tree (RDT)
| dc.contributor.author | Yellasiri, Ramadevi | |
| dc.contributor.author | Rao, C. R. | |
| dc.contributor.author | RamaKrishna, Hari | |
| dc.contributor.author | Prathima, T. | |
| dc.date.accessioned | 2022-03-27T06:00:20Z | |
| dc.date.available | 2022-03-27T06:00:20Z | |
| dc.date.issued | 2008-12-01 | |
| dc.description.abstract | New approaches to compute predominant attributes (referred as reduct) are discussed in this study. Rough Sets concepts and 'val' theory are adopted in this process. Procedure to construct a decision tree using these reduct is presented. These trees are referred as Reduct based Decision Tree (RDT). Decision rules for these RDTs are generated. 'Kappa statistics' was used to prove the efficiency of this model which is supported by K-fold test. © Medwell Journals, 2008. | |
| dc.identifier.citation | International Journal of Soft Computing. v.3(4) | |
| dc.identifier.issn | 18169503 | |
| dc.identifier.uri | https://dspace.uohyd.ac.in/handle/1/9077 | |
| dc.subject | Composite reduct | |
| dc.subject | Predominant attributes | |
| dc.subject | RDT and GPCR | |
| dc.subject | Rough sets | |
| dc.title | Reduct based decision tree (RDT) | |
| dc.type | Journal. Article | |
| dspace.entity.type |
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