Fuzzy quartile encoding as a preprocessing method for biomedical pattern classification

Abstract

A fuzzy set based preprocessing method is described that may be used in the classification of patterns. This method, dispersion-adjusted fuzzy quartile encoding, determines the respective degrees to which a feature (attribute) belongs to a collection of fuzzy sets that overlap at the respective quartile boundaries of the feature. The fuzzy sets are adjusted to take into account the overall dispersion of values for a feature. The membership values are subsequently used in place of the original feature value. This transformation has a normalizing effect on the feature space and is robust to feature outliers. This preprocessing method, empirically evaluated using five biomedical datasets, is shown to improve the discriminatory power of the underlying classifiers.