MACLAW: A modular approach for clustering with local attribute weighting
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Genetic algorithms for feature weighting: evolution vs. coevolution and darwin vs. lamarck
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
A coevolutionary approach for clustering with feature weighting application to image analysis
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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Feature weighting is known empirically to improve classification accuracy for k-nearest neighbor classifiers in tasks with irrelevant features. Many feature weighting algorithms are designed to work with symbolic features, or numeric features, or both, but cannot be applied to problems with features that do not fit these categories. This paper presents a new k-nearest neighbor feature weighting algorithm that works with any kind of feature for which a distance function can be defined. Applied to an image classification task with unusual set-like features, the technique improves classification accuracy significantly. In tests on standard data sets from the UCI repository, the technique yields improvements comparable to weighting features by information gain.