| dc.contributor.author | Çekik, Rasim | |
| dc.contributor.author | Telceken, Sedat | |
| dc.date.accessioned | 2019-10-21T20:10:59Z | |
| dc.date.available | 2019-10-21T20:10:59Z | |
| dc.date.issued | 2018 | |
| dc.identifier.issn | 1434-9922 | |
| dc.identifier.uri | https://dx.doi.org/10.1007/978-3-319-75408-6_4 | |
| dc.identifier.uri | https://hdl.handle.net/11421/20023 | |
| dc.description.abstract | The presence of missing value in a dataset can affect the performance of an analysis system such as classifier. To solve this problem many methods have been proposed in different studies using different theorems, analysis systems and methods such as Neural Network (NN), k-Nearest Neighbor (k-NN), closest fit etc. In this paper, we propose novel method based on RST for solving the problem of missing value that was lost (e.g., was erased). After dataset filling with proposed method, it has been observed improvement the performance of used analysis systems | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Springer Verlag | en_US |
| dc.relation.isversionof | 10.1007/978-3-319-75408-6_4 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Data Mining | en_US |
| dc.subject | Missing Value | en_US |
| dc.subject | Rough Set | en_US |
| dc.title | New method based on rough set for filling missing value | en_US |
| dc.type | bookPart | en_US |
| dc.relation.journal | Studies in Fuzziness and Soft Computing | en_US |
| dc.contributor.department | Anadolu Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
| dc.identifier.volume | 361 | en_US |
| dc.identifier.startpage | 41 | en_US |
| dc.identifier.endpage | 48 | en_US |
| dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
