Name
Abstract | ||
|---|---|---|
In machine learning and knowledge discovery, rough set theory is a useful tool to be employed as a preprocessing step for dimension reduction. However, for a given system, there may be more than one reduct to be selected. Different reducts will lead to discovered knowledge, which may be concise, precise, general, understandable and practically useful in different levels. It is a crucial issue to select the most suitable features or properties of the objects in a dataset in the machine learning process. In this paper, some external information is added to information system and may be simply regarded as user preference on attributes. Consequently, it will guide the procedure of retrieving reducts, which will give birth to the reduct subject to preference order of attributes. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-73451-2_22 | RSEISP |
Keywords | Field | DocType |
external information,user preference,different level,reduct subject,knowledge discovery,information system,different reducts,useful tool,preference order,retrieving reducts,dimension reduction,rough set theory,reduct,machine learning | Information system,Data mining,Reduct,Dimensionality reduction,Rough set,Preprocessor,Artificial intelligence,Knowledge extraction,Preference learning,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
4585 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofeng Zhang | 1 | 44 | 4.84 |
| Yongsheng Zhao | 2 | 75 | 19.66 |
| Zou Hailin | 3 | 45 | 4.70 |