Classification using an adaptive fuzzy network

The analysis of feature variance is a common approach used for data interpretation. In the case of pattern classification, however, the transformation of correlated features into a new set of uncorrelated variables must be used with caution, as there is no necessary causal connection between discriminatory power and variance. To compensate for this potential shortcoming, we present a classification method that blends variance analysis with an adaptive fuzzy logic network that identifies the most discriminatory set of uncorrelated variables. We empirically evaluate the effectiveness of this method using a suite of biomedical datasets and comparing its performance against two benchmark classifiers.