Document Type

Conference Proceeding

Publication Date


Publication Title

Tech Report TR99-1735


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.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


© Nicholas Howe


Author’s submitted manuscript.



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