LUCA BALDINI

PhD Graduate

PhD program:: XXXIV


Thesis title: Evolutionary Graph Classification Systems by Granular Computing based Embedding

Graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. Despite their invaluable descriptive power, their arbitrarily complex structured nature poses serious challenges when they are involved in learning systems. Typical approaches aim at building a vectorial representation of the graph in a suitable embedding space by leveraging on the selection of relevant prototypes that enable the use of common pattern recognition methods. An interesting paradigm able to synthesize prototypes in a data-driven fashion can be found in Granular Computing. This thesis investigates and develops novel techniques for graph embedding methods based on Granular Computing and Multi-Agent based systems in order to solve Pattern Recognition problems. Initially, the proposed methods aim at improving different aspects of an established Granular Computing-based framework designed for graph classification concerning the computational complexity, granulation and embedding ability and optimization problem of the training phase. A lightweight stochastic procedure for the selection of prototypes has been designed in order to mitigate the computational burden of the algorithm. Other proposed techniques focus on improving the granulation phase of the framework by selecting detailed granules of information that characterize the classes of the problem at hand. Concerning the embedding phase, six different graph embedding techniques inspired by the dissimilarity space embedding are proposed to represent graphs into meaningful embedding spaces. Concerning the optimization phase, a novel evolutionary-based approach has been designed for equipping the framework with a class-specific metric learning strategy together with a reformulation in a multi-objective fashion of the problem which aims at jointly optimizing the performance of the classifier, the number of information granules and the structural complexity of the classification model. In the second part of the work, a novel prototypical system for graph classification problems inspired to Multi-Agent Systems principles has been presented. The proposed system investigates a cooperative approach between different groups of agents for synthesizing meaningful granules of information and in turn enabling the graph embedding process via the Granular Computing paradigm. Different publicly available real-world benchmark datasets have been selected in order to show the effectiveness of the proposed methods in comparison with state-of-the-art graph-based classification systems.

Research products

11573/1597235 - 2022 - A class-specific metric learning approach for graph embedding by information granulation
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 01a Articolo in rivista
paper: APPLIED SOFT COMPUTING (AMSTERDAM: Elsevier Science) pp. 108199- - issn: 1568-4946 - wos: WOS:000736968800013 (1) - scopus: 2-s2.0-85121127035 (1)

11573/1657770 - 2022 - A multi-objective optimization approach for the synthesis of granular computing-based classification systems in the graph domain
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 01a Articolo in rivista
paper: SN COMPUTER SCIENCE (Singapore: Springer Singapore) pp. 1-28 - issn: 2661-8907 - wos: (0) - scopus: 2-s2.0-85135736001 (1)

11573/1643090 - 2022 - Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers: An Extended Analysis
Granato, Giuseppe; Martino, Alessio; Baldini, Luca; Rizzi, Antonello - 01a Articolo in rivista
paper: SN COMPUTER SCIENCE (Singapore: Springer Nature) pp. 1-17 - issn: 2662-995X - wos: (0) - scopus: 2-s2.0-85130905749 (3)

11573/1643077 - 2022 - On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study
Martino, Alessio; Baldini, Luca; Rizzi, Antonello - 01a Articolo in rivista
paper: ALGORITHMS (Molecular Diversity Preservation Int. (Basel, Switzerland)) pp. 1-19 - issn: 1999-4893 - wos: WOS:000802454200001 (2) - scopus: 2-s2.0-85129808497 (6)

11573/1661081 - 2022 - Ottimizzazione di sistemi lightweight granular computing per la classificazione di grafi etichettati
Rizzi, Antonello; Martino, Alessio; Baldini, Luca; De Santis, Enrico; Frattale Mascioli, Fabio Massimo - 04d Abstract in atti di convegno
conference: XXXVI Riunione Nazionale dei Ricercatori di Elettrotecnica (Ancona, Italia)
book: Memorie - XXXVI Riunione Nazionale dei Ricercatori di Elettrotecnica - ()

11573/1568482 - 2021 - Towards a class-aware information granulation for graph embedding and classification
Baldini, L.; Martino, A.; Rizzi, A. - 04b Atto di convegno in volume
conference: 11th International Joint Conference on Computational Intelligence, IJCCI 2019 (Vienna; Austria)
book: Studies in Computational Intelligence - (978-3-030-70593-0; 978-3-030-70594-7)

11573/1584576 - 2021 - Relaxed dissimilarity-based symbolic histogram variants for granular graph embedding
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 13th International Joint Conference on Computational Intelligence (Online streaming)
book: Proceedings of the 13th International Joint Conference on Computational Intelligence - Volume 1: NCTA - (978-989-758-534-0)

11573/1584568 - 2021 - A multi-agent approach for graph classification
Baldini, Luca; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 13th International Joint Conference on Computational Intelligence (Online streaming)
book: Proceedings of the 13th International Joint Conference on Computational Intelligence - Volume 1: NCTA - ()

11573/1584580 - 2021 - A physically inspired equivalent neural network circuit model for SoC estimation of electrochemical cells
Leonori, Stefano; Baldini, Luca; Rizzi, Antonello; Frattale Mascioli, Fabio Massimo - 01a Articolo in rivista
paper: ENERGIES (Basel : Molecular Diversity Preservation International) pp. - - issn: 1996-1073 - wos: WOS:000719070700001 (8) - scopus: 2-s2.0-85118716475 (8)

11573/1453613 - 2020 - Exploiting cliques for granular computing-based graph classification
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 2020 International Joint Conference on Neural Networks, IJCNN 2020 (Glasgow (UK))
book: Proceedings of the International Joint Conference on Neural Networks - (978-1-7281-6926-2)

11573/1461448 - 2020 - Complexity vs. performance in granular embedding spaces for graph classification
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 04b Atto di convegno in volume
conference: Proceedings of the 12th International Joint Conference on Computational Intelligence - NCTA (Online Streaming)
book: Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA - (978-989-758-475-6)

11573/1453658 - 2020 - Facing Big Data by an agent-based multimodal evolutionary approach to classification
Giampieri, Mauro; Baldini, Luca; De Santis, Enrico; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 2020 International Joint Conference on Neural Networks, IJCNN 2020 (Glasgow (UK))
book: Proceedings of the International Joint Conference on Neural Networks - (978-1-7281-6926-2)

11573/1461458 - 2020 - Intrusion detection in wi-fi networks by modular and optimized ensemble of classifiers
Granato, Giuseppe; Martino, Alessio; Baldini, Luca; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 12th International Joint Conference on Computational Intelligence - NCTA (Online Streaming)
book: Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA - (978-989-758-475-6)

11573/1327765 - 2019 - Stochastic information granules extraction for graph embedding and classification
Baldini, Luca; Martino, Alessio; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 11th International Joint Conference on Computational Intelligence, IJCCI 2019 (Vienna, Austria)
book: IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence - ()

11573/1327846 - 2019 - Calibration techniques for binary classification problems. A comparative analysis
Martino, Alessio.; De Santis, Enrico.; Baldini, Luca; Rizzi, Antonello - 04b Atto di convegno in volume
conference: 11th International Joint Conference on Computational Intelligence, IJCCI 2019 (Vienna; Austria)
book: IJCCI 2019 - Proceedings of the 11th International Joint Conference on Computational Intelligence - (978-989758384-1)

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