Ricerc@Sapienza

Dissimilarity spaces, along with feature reduction/ selection techniques, are among the mainstream approaches when dealing with pattern recognition problems in structured (and possibly non-metric) domains. In this work, we aim at investigating dissimilarity space representations in a biology-related application, namely protein function classification, as proteins are a seminal example of structured data given their primary and tertiary structures.

n this paper we present an interesting application of the Decision Support System, known as the OCC_System, designed for faults recognition and classification within the real-world Medium Voltage power grid of Rome, Italy, managed by the Azienda Comunale Energia e Ambiente (ACEA) company. Given a historical data set consisting of fault patterns described by heterogeneous features related to endogenous and exogenous factors, the recognition system is trained to classify fault states assigning them a probability of fault.