DocumentCode :
2892909
Title :
Machine Learning and Text Mining of Trophic Links
Author :
Milani, G.A. ; Bohan, D. ; Dunbar, Steven ; Muggleton, S. ; Raybould, A. ; Tamaddoni-Nezhad, A.
Author_Institution :
Dept. of Comput., Imperial Coll. London, London, UK
Volume :
2
fYear :
2012
fDate :
12-15 Dec. 2012
Firstpage :
410
Lastpage :
415
Abstract :
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.
Keywords :
Web sites; data mining; expert systems; food processing industry; learning (artificial intelligence); text analysis; FSE data; Vortis data; automatic probabilistic food-Web generation; domain experts; farm scale evaluation data; hypothetical trophic links; initial Vortis food web; machine learning; manual corroboration; pitfall data; text mining; Accuracy; Correlation; Machine learning; Manuals; Probabilistic logic; Text mining; Tunneling magnetoresistance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4673-4651-1
Type :
conf
DOI :
10.1109/ICMLA.2012.201
Filename :
6406770
Link To Document :
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