Symbolic Nearest Mean Classifiers

Piew Datta, Dennis Kibler

The minimum-distance classifier summarizes each class with a prototype and then uses a nearest neighbor approach for classification. Three drawbacks of the original minimum-distance classifier are its inability to work with symbolic attributes, weigh attributes, and learn more than a single prototype for each class. The proposed solutions to these problems include defining the mean for symbolic attributes, providing a weighting metric, and learning several possible prototypes for each class. The learning algorithm developed to tackle these problems, SNMC, increases classification accuracy by 10% over the original minimum-distance classifier and has a higher average generalization accuracy than both C4.5 and PEBLS on 20 domains from the UCI data repository.


This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.