Abstract
The ILP system Progol is incomplete in not being able to generalise a single example to multiple clauses. This limitation is referred as single-clause learning (SCL) in this paper. However, according to the Blumer bound, incomplete learners such as Progol can have higher predictive accuracy while use less search than more complete learners. This issue is particularly relevant in real-world problems, in which it is unclear whether the unknown target theory or its approximation is within the hypothesis space of the incomplete learner. This paper uses two real-world applications in systems biology to study whether it is necessary to have complete multi-clause learning (MCL) methods, which is computationally expensive but capable of deriving multi-clause hypotheses that is in the systems level. The experimental results show that in both applications there do exist datasets, in which MCL has significantly higher predictive accuracies than SCL. On the other hand, MCL does not outperform SCL all the time due to the existence of the target hypothesis or its approximations within the hypothesis space of SCL.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Syngenta Ltd., http://www.syngenta.com/en/index.html
Blumer, A., Ehrenfeucht, A., Haussler, D., Warmuth, M.K.: Occam’s razor. Information Processing Letters 24(6), 377–380 (1987)
Centeno, D.C., Osorio, S., et al.: Malate plays a crucial role in starch metabolism, ripening, and soluble solid content of tomato fruit and affects postharvest softening. Plant Cell 23, 162–184 (2011)
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: ICLP 2010 Technical Communications. Springer, Berlin (2010)
Waterman, C.L., et al.: An integrated functional genomic study of acute phenobarbital exposure in the rat. BMC Genomics 11(1), 9 (2010)
Fernie, A.R., Carrari, F., Sweetlove, L.J.: Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport. Current Opinion in Plant Biology 7, 254–261 (2004)
Inoue, K.: Induction as consequence finding. Machine Learning 55, 109–135 (2004)
Inoue, K., Sato, T., Ishihata, M., et al.: Evaluating abductive hypotheses using an EM algorithm on BDDs. In: IJCAI 2009, pp. 810–815 (2009)
LycoCyc. Solanum lycopersicum database, http://solcyc.solgenomics.net//LYCO/
Muggleton, S.H.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)
Muggleton, S.H., Bryant, C.H.: Theory Completion Using Inverse Entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 130–146. Springer, Heidelberg (2000)
Muggleton, S.H., Chen, J., Watanabe, H., Dunbar, S.J., Baxter, C., Currie, R., Salazar, J.D., Taubert, J., Sternberg, M.J.E.: Variation of Background Knowledge in an Industrial Application of ILP. In: Frasconi, P., Lisi, F.A. (eds.) ILP 2010. LNCS, vol. 6489, pp. 158–170. Springer, Heidelberg (2011)
Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A.: MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS (LNAI), vol. 7207, pp. 239–255. Springer, Heidelberg (2012)
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl. Acids Res. 27(1), 29–34 (1999)
Ray, O.: Nonmonotonic abductive inductive learning. Journal of Applied Logic 7(3), 329–340 (2009)
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 (LNAI), vol. 7207, pp. 319–333. Springer, Heidelberg (2012)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.H.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)
Valiant, L.: A theory of the learnable. Journal of the ACM 27(11), 1134–1142 (1984)
Yamamoto, A.: Which Hypotheses can be Found with Inverse Entailment? In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 296–308. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, D. et al. (2012). Does Multi-Clause Learning Help in Real-World Applications?. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-31951-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31950-1
Online ISBN: 978-3-642-31951-8
eBook Packages: Computer ScienceComputer Science (R0)