Document Type
Conference Proceeding
Publication Date
1999
Publication Title
Tech Report TR99-1735
Abstract
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© Nicholas Howe
Recommended Citation
Howe, Nicholas and Cardie, Claire, "Weighting Unusual Feature Types" (1999). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/104
Comments
Author’s submitted manuscript.