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- W3091390489 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." @default.
- W3091390489 created "2020-10-08" @default.
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- W3091390489 date "2020-07-01" @default.
- W3091390489 modified "2023-09-23" @default.
- W3091390489 title "On the Optimization of Embedding Spaces via Information Granulation for Pattern Recognition" @default.
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- W3091390489 doi "https://doi.org/10.1109/ijcnn48605.2020.9206830" @default.
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