Abstract
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 di.erent 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.
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Pham, T.D. (2005). An Optimally Weighted Fuzzy k-NN Algorithm. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_26
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DOI: https://doi.org/10.1007/11551188_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
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