Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical models to classify them, based on various graph distances. Due to the complexity of the models, made of tens of thousands of nodes and edges, we focus on model simplification solutions to reduce execution times, still maintaining high accuracy. Experimental results on three datasets of biological interest show the achieved performance improvements.
Model simplification for supervised classification of metabolic networks
Guarracino M. R.
Membro del Collaboration Group
;
2020-01-01
Abstract
Many real applications require the representation of complex entities and their relations. Frequently, networks are the chosen data structures, due to their ability to highlight topological and qualitative characteristics. In this work, we are interested in supervised classification models for data in the form of networks. Given two or more classes whose members are networks, we build mathematical models to classify them, based on various graph distances. Due to the complexity of the models, made of tens of thousands of nodes and edges, we focus on model simplification solutions to reduce execution times, still maintaining high accuracy. Experimental results on three datasets of biological interest show the achieved performance improvements.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
