Evolving meta-correlation classes for binary similarity

In the field of machine learning and pattern recognition, the use of binary correlation indices is essential for accurate prediction and modelling. This work presents a novel evolutionary method to address the problem of discovering binary correlation indices in different application domains. The proposed approach introduces the concept of meta-correlation, a parametric formula representing classes of binary similarity indices, and optimizes it through an evolutionary scheme. The method has been experimented with and validated in the context of the link prediction problem based on local topological similarity (i.e. graph neighbourhood). A Differential Evolution optimization algorithm finds the evolved correlations that perform best in a given domain. Experiments conducted across different network domains have shown that the instances of the discovered meta-correlations generally outperform state-of-the-art binary correlation indices for all the experimented domains. This approach effectively explores the correlation space and can find a unique pattern that adapts to the domains under consideration. The meta-correlation classes can be applied to both topological and semantic similarity problems, taking into account local information without requiring complete knowledge of the graph.