Abstract
Tree kernels are an effective method to capture the structural information of tree data of various applications and many algorithms have been proposed. Nevertheless, we do not have sufficient knowledge about how to select good kernels. To answer this question, we focus on 32 tree kernel algorithms defined within a certain framework to engineer positive definite kernels, and investigate them under two different parameter settings. The result is amazing. Three of the 64 tree kernels outperform the others, and their superiority proves statistically significant through t-tests. These kernels include the benchmark tree kernels proposed in the literature, while many of them are introduced and tested for the first time in this paper.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Augsten, N., Böhlen, M.H., Gamper, J.: The pq-gram distance between ordered labeled trees. ACM Trans. Database Syst. 35(1), 1–36 (2010)
Collins, M., Duffy, N.: Convolution kernels for natural language. In: Proceedings of Advances in Neural Information Processing Systems 14 (NIPS), pp. 625–632 (2001)
Cristianini, N., Shawe-Taylor, J.: An introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Demaine, E.D., Mozes, S., Rossman, B., Weimann, O.: An optimal decomposition algorithm for tree edit distance. ACM Trans. Algorithms (TALG) 6(1), 2:1–2:19 (2009)
Hashimoto, K., Goto, S., Kawano, S., Aoki-Kinoshita, K.F., Ueda, N.: Kegg as a glycome informatics resource. Glycobiology 16, 63R–70R (2006)
Haussler, D.: Convolution kernels on discrete structures. UCSC-CRL 99–10, Department of Computer Science, University of California at Santa Cruz (1999)
Kashima, H., Koyanagi, T.: Kernels for semi-structured data. In: Proceedings of the 9th International Conference on Machine Learning (ICML), pp. 291–298 (2002)
Kimura, D., Kashima, H.: Computation of subpath kernel for trees. In: Proceedings of the 29th International Conference on Machine Learning (ICML) (2012)
Kuboyama, T., Shin, K., Kashima, H.: Flexible tree kernels based on counting the number of tree mappings. In Proceedings of the Machine Learning with Graphs (MLG) (2006)
Kuboyama, T., Hirata, K., Aoki-Kinoshita, K.F.: An efficient unordered tree kernel and its application to glycan classification. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 184–195. Springer, Heidelberg (2008)
Kuboyama, T., Hirata, K., Aoki-Kinoshita, K.F., Kashima, H., Yasuda, H.: A gram distribution kernel applied to glycan classification and motif extraction. Genome Inform. Ser. 17(2), 25–34 (2006)
Kuboyama, T., Hirata, K., Kashima, H., Aoki-Kinoshita, K.F., Yasuda, H.: A spectrum tree kernel. Inf. Media Technol. 2(1), 292–299 (2007)
Lu, C.L., Su, Z.-Y., Tang, C.Y.: A new measure of edit distance between labeled trees. In: Wang, J. (ed.) COCOON 2001. LNCS, vol. 2108, pp. 338–348. Springer, Heidelberg (2001)
Lu, S.Y.: A tree-to-tree distance and its application to cluster analysis. EEE Trans. Pattern Anal. Mach. Intell. (PAMI) 1, 219–224 (1979)
Moschitti, A.: Example data for Tree Kernels in SVM-light. http://disi.unitn.it/moschitti/Tree-Kernel.htm
Pyysalo, S., Airola, A., Heimonen, J., Bjorne, J., Ginter, F., Salakoski, T.: Comparative analysis of five protein-protein interaction corpora. BMC Bioinform. 9(S–3), S6 (2008)
Shin, K., Cuturi, M., Kuboyama, T.: Mapping kernels for trees. In: Proceedings of the 28th International Conference on Machine Learning ICML (2011)
K. Shin and T. Kuboyama. A generalization of Haussler’s convolution kernel - mapping kernel. In: Proceedings of the 25th International Conference on Machine Learning ICML (2008)
Taï, K.C.: The tree-to-tree correction problem. JACM 26(3), 422–433 (1979)
Zaki, M.J., Aggarwal, C.C.: Xrules: an effective algorithm for structural classification of XML data. Mach. Learn. 62, 137–170 (2006)
Zhang, K.: Algorithms for the constrained editing distance between ordered labeled trees and related problems. Pattern Recogn. 28(3), 463–474 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Shin, K., Kuboyama, T. (2014). A Comprehensive Study of Tree Kernels. In: Nakano, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2013. Lecture Notes in Computer Science(), vol 8417. Springer, Cham. https://doi.org/10.1007/978-3-319-10061-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-10061-6_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10060-9
Online ISBN: 978-3-319-10061-6
eBook Packages: Computer ScienceComputer Science (R0)