A hybrid approach for breast tissue data classification

This article presents a hybrid approach for breast tissue classification problem, where a limited dataset is available and misclassification may have severe adverse implications. Besides using classical methods in a two-stage classification setup, the method employs detailed data analysis, selection, and manipulation before each stage of classification to yield nearly zero false negative classification rate. The integration of data analysis methods within the structure of hybrid classification approach is the main strength of the proposed method.