Abstract
Fuzzy class label adjustment is a classification preprocessing strategy that compensates for the possible imprecision of class labels. Using training vectors, robust measures of location and dispersion are computed for each class center. Based on distances from these centers, fuzzy sets are constructed that determine the degree to which each input vector belongs to each class. These membership values are then used to adjust class labels for the training vectors. This strategy is evaluated using a multilayer perceptron and two different robust location measures for the discrimination of meteorological storm events and is shown to improve the performance of the underlying classifier.