MultiResolution Complexity Analysis. A Novel Method for Partitioning Datasets into Regions of Different Classification Complexity

Systems for complexity estimation typically aim to quantify the overall complexity of a domain, with the goal of comparing the hardness of different datasets or to associate a classification task to an algorithm that is deemed best suited for it. In this work we describe MultiResolution Complexity Analysis, a novel method for partitioning a dataset into regions of different classification complexity, with the aim of highlighting sources of complexity or noise inside the dataset. Initial experiments have been carried out on relevant datasets, proving the effectiveness of the proposed method.