Inferring understandable rules through digital synthesis

The extraction of a set of rules underlying a classification problem is performed by applying a new algorithm reconstructing the AND-OR expression of any Boolean function from a given set of samples. The basic kernel of the method, called Hamming Clustering (HC), is the generation of clusters of input patterns that belong to the same class and are close each other according to the Hamming distance. Inputs are identified and neglected, which do not influence the final output, thus automatically reducing the complexity of the final set of rules. The performances of HC are evaluated through artificial and real-world benchmarks: its application to the breast cancer prognosis leads to the derivation of a small set of rules solving the associated classification problem.