Evolutionary weighting of image features for diagnosing of CNS tumors

This paper concerns an application of evolutionary feature weighting for diagnosis support in neuropathology. The original data in the classification task are the microscopic images of ten classes of central nervous system (CNS) neuroepithelial tumors. These images are segmented and described by the features characterizing regions resulting from the segmentation process. The final features are in part irrelevant. Thus, we employ an evolutionary algorithm to reduce the number of irrelevant attributes, using the predictive accuracy of a classifier ('wrapper' approach) as an individual's fitness measure. The novelty of our approach consists in the application of evolutionary algorithm for feature weighting, not only for feature selection. The weights obtained give quantitative information about the relative importance of the features. The results of computational experiments show a significant improvement of predictive accuracy of the evolutionarily found feature sets with respect to the original feature set.