Embedding spaces are one of the mainstream approaches when dealing with structured data. Granular Computing, in the last decade, emerged as a powerful paradigm for the automatic synthesis of embedding spaces that, at the same time, yield an interpretable model on the top of meaningful entities known as information granules. Usually, in these contexts, one aims at finding the smallest set of information granules in order to boost the model interpretability while keeping satisfactory performances. In this paper, we add a third objective, namely the structural complexity of the resulting model and we exploit three biology-related case studies related to metabolic networks and protein networks in order to investigate the link between classification performances, embedding space dimensionality and structural complexity of the resulting model.

On the optimization of embedding spaces via information granulation for pattern recognition / Martino, Alessio; Massimo Frattale Mascioli, Fabio; Rizzi, Antonello. - Proceedings of the International Joint Conference on Neural Networks, (2020), pp. 1-8. (2020 International Joint Conference on Neural Networks, IJCNN 2020, Online Event due to COVID-19 (formerly Glasgow, UK), 19-24 July 2020). [10.1109/IJCNN48605.2020.9206830].

On the optimization of embedding spaces via information granulation for pattern recognition

Alessio Martino
;
2020

Abstract

Embedding spaces are one of the mainstream approaches when dealing with structured data. Granular Computing, in the last decade, emerged as a powerful paradigm for the automatic synthesis of embedding spaces that, at the same time, yield an interpretable model on the top of meaningful entities known as information granules. Usually, in these contexts, one aims at finding the smallest set of information granules in order to boost the model interpretability while keeping satisfactory performances. In this paper, we add a third objective, namely the structural complexity of the resulting model and we exploit three biology-related case studies related to metabolic networks and protein networks in order to investigate the link between classification performances, embedding space dimensionality and structural complexity of the resulting model.
2020
978-1-7281-6926-2
computational biology, embedding spaces, granular computing, support vector machine, systems biology, topological data analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/214537
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