An optimally weighted fuzzy k-nn algorithm

Pham, Tuan D. (2005) An optimally weighted fuzzy k-nn algorithm. In: Proceedings of the 3rd International Conference on Advances in Pattern Recognition 2005 (3686) pp. 239-247. From: 3rd International Conference on Advances in Pattern Recognition 2005, 22-25 August 2005, Bath, United Kingdom.

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The nearest neighbor rule is a non-parametric approach and has been widely used for pattern classification. The k-nearest neighbor (k-NN) rule assigns crisp memberships of samples to class labels; whereas the fuzzy k-NN neighbor rule replaces crisp memberships with fuzzy memberships. The membership assignment by the conventional fuzzy k-NN algorithm has a disadvantage in that it depends on the choice of some distance function, which is not based on any principle of optimality. To overcome this problem, we introduce in this paper a computational scheme for determining optimal weights to be combined with different fuzzy membership grades for classification by the fuzzy k-NN approach. We show how this optimally weighted fuzzy k-NN algorithm can be effectively applied for the classification of microarray-based cancer data.

Item ID: 14803
Item Type: Conference Item (Research - E1)
ISBN: 0302-9743
ISSN: 9783540287575
Date Deposited: 08 Nov 2010 00:54
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%
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