Please use this identifier to cite or link to this item:
|Title:||Fuzzy-rough nearest neighbors algorithm||Authors:||Sarkar, Manish||Issue Date:||2000||Citation:||Sarkar, Manish (2000). Fuzzy-rough nearest neighbors algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 5 : 3556-3561. ScholarBank@NUS Repository.||Abstract:||In this paper the classification efficiency of the conventional K-nearest neighbors algorithm is enhanced by exploiting the fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbors algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the generated class confidence values, which are interpreted in terms of the fuzzy-rough ownership values, do not necessarily summed up to one. Consequently, the proposed algorithm can distinguish between equal evidence and ignorance, and thus makes the semantics of the class confidence values richer.||Source Title:||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics||URI:||http://scholarbank.nus.edu.sg/handle/10635/40756||ISSN:||08843627|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 28, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.