- Title
- Capped K-NN Editing in definition lacking environments
- Creator
- Stranieri, Andrew; Yatsko, Andrew; Golden, Isaac; Mammadov, Musa; Bagirov, Adil
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/71255
- Identifier
- vital:6730
- Identifier
- ISSN:1558-884X
- Abstract
- While any input may be contributing, imprecise specification of class of data subdivided into classes identifies as rather common a source of noise. The misrepresentation may be characteristic of the data or be caused by forcing of a regression problem into the classification type. Consideration is given to examples of this nature, and an alternative is proposed. In the main part, the approach is based on a well-known technique of data treatment for noise using k-NN. The paper advances an editing technique designed around idea of variable number of authenticating instances. Test runs performed on publicly available and proprietary data demonstrate high retention ability of the new procedure without loss of classification accuracy. Noise reduction methods in a broader classification context are extensively surveyed.
- Relation
- Journal of Pattern Recognition Research Vol. 8, no. 1 (2013), p. 39-58
- Rights
- c 2013 JPRR. All rights reserved. Permissions to make digital or hard copies of all or part of this work for personal or classroom use may be granted by JPRR provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or to republish, requires a fee and/or special permission from JPRR
- Rights
- Open access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Class noise; Data editing; Instance selection; Fuzzy classification
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