Abstract
In a previous paper we described a machine learning approach which was used to automatically generate food-webs from national-scale agricultural data. The learned food-webs in the previous study consist of hundreds of ground facts representing trophic links between individual species. These species food-webs can be used to explain the structure and dynamics of particular eco-systems, however, they cannot be directly used as general predictive models. In this paper we describe the first steps towards this generalisation and present initial results on (i) learning general functional food-webs (i.e. trophic links between functional groups of species) and (ii) meta-interpretive learning (MIL) of general predictive rules (e.g. about the effect of agricultural management). Experimental results suggest that functional food-webs have at least the same levels of predictive accuracies as species food-webs despite being much more compact. In this paper we also present initial experiments where predicate invention and recursive rule learning in MIL are used to learn food-webs as well as predictive rules directly from data.
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Notes
- 1.
Available from: http://www.doc.ic.ac.uk/~shm/Software/progol5.0/.
- 2.
Available from: http://ilp.doc.ic.ac.uk/metagolD_MLJ/.
References
Berardi, M., Malerba, D.: Learning recursive patterns for biomedical information extraction. In: Muggleton, S.H., Otero, R., Tamaddoni-Nezhad, A. (eds.) ILP 2006. LNCS (LNAI), vol. 4455, pp. 79–93. Springer, Heidelberg (2007)
Bohen, D.A., Caron-Lormier, G., Muggleton, S.H., Raybould, A., Tamaddoni-Nezhad, A.: Automated discovery of food webs from ecological data using logic-based machine learning. PloS ONE 6(12), e29028 (2011)
Caron-Lormier, G., Bohan, D.A., Hawes, C., Raybould, A., Haughton, A.J., Humphry, R.W.: How might we model an ecosystem? Ecol. Model. 220(17), 1935–1949 (2009)
Davey, J., Vaughan, I., King, R.A., Bell, J., Bohan, D., Bruford, M., Holland, J., Symondson, W.: Intraguild predation in winter wheat: prey choice by a common epigeal carabid consuming spiders. J. Appl. Ecol. 50(1), 271–279 (2013)
Dietterich, T.G.: Machine learning in ecosystem informatics and sustainability. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI, Pasadena, Calif., pp. 8–13 (2009)
Haughton, A.J., Champion, G.T., Hawes, C., Heard, M.S., Brooks, D.R., Bohan, D.A., Clark, S.J., Dewar, A.M., Firbank, L.G., Osborne, J.L., et al.: Invertebrate responses to the management of genetically modified herbicide-tolerant and conventional spring crops. ii. within-field epigeal and aerial arthropods. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 358(1439), 1863 (2003)
Inoue, K., Furukawa, K., Kobayashi, I., Nabeshima, H.: Discovering rules by meta-level abduction. In: De Raedt, L. (ed.) ILP 2009. LNCS, vol. 5989, pp. 49–64. Springer, Heidelberg (2010)
Muggleton, S.H., Lin, D.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. In: Proceedings of the 23rd International Joint Conference Artificial Intelligence (IJCAI 2013), pp. 1551–1557 (2013)
Muggleton, S.H., Lin, D., Pahlavi, N., Tamaddoni-Nezhad, A.: Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94, 25–49 (2014)
Muggleton, S.H., Bryant, C.H.: Theory completion using inverse entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, p. 130. Springer, Heidelberg (2000)
Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. 100, 49–73 (2015). doi:10.1007/s10994-014-5471-y
Tamaddoni-Nezhad, A., Bohan, D., Raybould, A., Muggleton, S.H.: Machine learning a probabilistic network of ecological interactions. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS, vol. 7207, pp. 332–346. Springer, Heidelberg (2012)
Tamaddoni-Nezhad, A., Milani, G., Raybould, A., Muggleton, S., Bohan, D.: Construction and validation of food-webs using logic-based machine learning and text-mining. Adv. Ecol. Res. 49, 225–289 (2013)
Acknowledgements
The authors thank the members of Syngenta University Innovation Centre (UIC) at Imperial College, in particular Stuart Dunbar for his encouragement and support. We also thank Guy Woodward, Christian Mulder, Michael Traugott and Antonis Kakas for helpful discussions and support and the anonymous referees for useful comments. The first author acknowledges the support of an EPSRC “Pathways to Impact Award” during the writing of this paper.
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Tamaddoni-Nezhad, A., Bohan, D., Raybould, A., Muggleton, S. (2015). Towards Machine Learning of Predictive Models from Ecological Data. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_11
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