Biomedical data analysis using dispersion-adjusted fuzzy quantile encoding
Nick J. Pizzi · 2008
A fuzzy set theoretic classification preprocessing method is described that may be used in the analysis and interpretation of biomedical data. This method, dispersion- adjusted fuzzy quantile encoding, determines the respective degrees to which a feature (attribute) belongs to a collection of fuzzy sets that overlap at the respective quantile 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 more robust to feature outliers. The effectiveness of this method, empirically demonstrated using three publicly available biomedical datasets, is shown to improve the discriminatory power of the underlying classifier.