Keywords
feature weighting, neural network
Abstract
In this work we propose a feature weighting method for classification tasks by extracting relevant information from a trained neural network. This method weights an attribute based on strengths (weights) of related links in the neural network, in which an important feature is typically connected to strong links and has more impact on the outputs. This method is applied to feature weighting br the nearest neighbor classifier and is tested on 15 real-world classification tasks. The results show that it can improve the nearest neighbor classifier on 14 of the 15 tested tasks, and also outperforms the neural network on 9 tasks.
Original Publication Citation
Zeng, X., and Martinez, T. R., "Feature Weighting Using Neural Networks", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'4, pp. 327-133, 24.
BYU ScholarsArchive Citation
Martinez, Tony R. and Zeng, Xinchuan, "Feature Weighting Using Neural Networks" (2004). Faculty Publications. 1030.
https://scholarsarchive.byu.edu/facpub/1030
Document Type
Peer-Reviewed Article
Publication Date
2004-07-29
Permanent URL
http://hdl.lib.byu.edu/1877/2423
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information
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