Variance analysis and biomedical pattern classification

Component analysis is a common method used for the interpretation of data; however, in the case of pattern classification, the transformation of possibly correlated features into a new set of uncorrelated variables, must be used with caution since a principal component, which may account for significant variance in the data, is not necessarily discriminatory. To compensate for this deficiency, we present a classification method using an adaptive network of fuzzy logic connectives to select the most discriminatory principal components. We empirically evaluate the effectiveness of this classification method using a suite of biomedical datasets and comparing its performance against a set of benchmark classifiers.

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