Network Inference of Dynamic Models by the Combination of Spanning Arborescences
| Autor: | Coutant, Anthony, Rouveirol, Céline |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Coutant, Anthony |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] Spanning Arborescences Model Averaging Network inference [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] Ensemble Learning [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | Journées Ouvertes en Biologie, Informatique et Mathématiques Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2017, Lille, France |
| Popis: | International audience; In this paper, we tackle the problem of generative learning of dynamic models from”fat” time series data (high #variables/#individuals ratio), leading to a high sensitivity of learnedmodels to the dataset noise. To overcome this problem, we propose a method computing a mixtureof many highly biased but optimal spanning arborescences obtained from many perturbed versionsof the original dataset, introducing variance to counterbalance the strong arborescence bias. Themethod is theoretically at the boundary between structure oriented Bayesian model averagingand recent work on density estimation using mixtures of poly-trees through a perturb and combineframework, transposed to a dynamic setting. In practice, preliminary results on the recent DREAMD8C1 challenge are promising. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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