Machine learning and text mining of trophic links

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Milani, Ghazal Afroozi | Bohan, David | Dunbar, Stuart | Muggleton, Stephen | Raybould, Alan | Tamaddoni-Nezhad, Alireza

Edité par CCSD ; IEEE Institute of Electrical and Electronics Engineers -

communication orale, short paper publié sur IEEE Xplore digital library (IEEE Conference Publications) EcolDur SPE. International audience. Machine Learning has been used to automatically generate a probabilistic food-web from Farm Scale Evaluation (FSE) data. The initial food web proposed by machine learning has been examined by domain experts and comparison with the literature shows that many of the links are corroborated. The FSE data were collected using two different sampling techniques, namely Vortis and pitfall. The corroboration of the initial Vortis food web, generated by machine learning, was performed manually by the domain experts. However, manual corroboration of hypothetical trophic links is difficult and requires significant amounts of time. In this paper we review the method and the main results on machine learning of trophic links. We study common trophic links from Vortis and pitfall data. We also describe a new method and present initial results on automatic corroboration of trophic links using text mining.

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