Improving the accuracy of the Euclidean distance classifier

The Euclidean distance (ED) classifier has the advantage of simplicity in design and fast computational speed, but has poor classification accuracy. Using a new feature normalization technique and feature weighting, a substantial increase in accuracy is obtained with no significant increase in computational cost or complexity of design.