An optimally weighted fuzzy k-nn algorithm
Pham, Tuan D. (2005) An optimally weighted fuzzy k-nn algorithm. Proceedings of the 3rd International Conference on Advances in Pattern Recognition 2005. 3rd International Conference on Advances in Pattern Recognition 2005 , 22-25 August 2005, Bath, United Kingdom , pp. 239-247.
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View at Publisher Website: http://dx.doi.org/10.1007/11551188_26
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 Type:||Conference Item (Refereed Research Paper - E1)|
|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%|
|Deposited On:||08 Nov 2010 10:54|
|Last Modified:||18 Jun 2013 01:22|
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