Ricerc@Sapienza

Granular Computing is a powerful information processing paradigm for synthesizing advanced pattern recognition systems in non-conventional domains. In this paper, a novel procedure for the automatic synthesis of suitable information granules is proposed. The procedure leverages a joint sensitivity-vs-specificity score that accounts the meaningfulness of candidate information granules for each class considered in the classification problem at hand.

Graphs are data structures able to efficiently describe real-world systems and, as such, have been extensively used in recent years by many branches of science, including machine learning engineering. However, the design of efficient graph-based pattern recognition systems is bottlenecked by the intrinsic problem of how to properly match two graphs. In this paper, we investigate a granular computing approach for the design of a general purpose graph-based classification system.