Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Automatic Feature Selection with Applications to Script Identification of Degraded Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pixel Position Regression - Application to Medical Image Segmentation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
An adaptive k-nearest neighbor text categorization strategy
ACM Transactions on Asian Language Information Processing (TALIP)
Cancer classification and prediction using logistic regression with Bayesian gene selection
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects
Information Sciences: an International Journal
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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 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.