Abstract
Sequence labeling is the task of assigning a label sequence to an observation sequence. Since many methods to solve this problem depend on the specification of predictive features, automated methods for their derivation are desirable. Unlike in other areas of pattern-based classification, however, no algorithm to directly mine class-correlated patterns for sequence labeling has been proposed so far. We introduce the novel task of mining class-correlated sequence patterns for sequence labeling and present a supervised pattern growth algorithm to find all patterns in a set of observation sequences, which correlate with the assignment of a fixed sequence label no less than a user-specified minimum correlation constraint. From the resulting set of patterns, features for a variety of classifiers can be obtained in a straightforward manner. The efficiency of the approach and the influence of important parameters are shown in experiments on several biological datasets.
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
Dietterich, T.G.: Machine learning for sequential data: a review. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 15–30. Springer, Heidelberg (2002)
Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of the IEEE 77(2), 257–286 (1989)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: probabilistic models for segmenting and labeling sequence data. In: Proc. of the 18th Int. Conf. on Machine Learning (ICML 2001), pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Vapnik, V.N.: Statistical learning theory. Wiley, New York (1998)
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6, 1453–1484 (2005)
Bringmann, B., Nijssen, S., Zimmermann, A.: Pattern-based classification: a unifying perspective. In: ECML/PKDD-09 Workshop From Local Patterns to Global Models (2009)
Birzele, F., Kramer, S.: A new representation for protein secondary structure prediction based on frequent patterns. Bioinformatics 22, 2628–2634 (2006)
Morishita, S., Sese, J.: Traversing itemset lattices with statistical metric pruning. In: Proc. of the 19th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2000), pp. 226–236. ACM, New York (2000)
Nijssen, S., Kok, J.N.: Multi-class correlated pattern mining, extended version. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 165–187. Springer, Heidelberg (2006)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Qiming, C., Dayal, U., Hsu, M.C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of the 17th Int. Conf. on Data Engineering (ICDE 2001), pp. 215–224. IEEE Computer Science, Washington (2001)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. of the 11th Int. Conf. on Data Engineering (ICDE 1995), pp. 3–14. IEEE Computer Society, Washington (1995)
Srikant, R., Agrawal, R.: Mining sequential patterns: generalisations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Int. Conf. on Very Large Data Bases (VLDB 1994), pp. 487–499. Morgan Kaufmann, San Francisco (1994)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Machine Learning 42, 31–60 (2001)
Ayres, J., Gehrke, J., Yiu, T., Flannik, J.: Sequential pattern mining using a bitmap representation. In: Proc. of the 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2002), pp. 429–435. ACM, New York (2002)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)
Bay, S.D., Pazzani, M.J.: Detecting change in categorical data: mining contrast sets. In: Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 1999), pp. 302–306. ACM, New York (1999)
Nijssen, S., Guns, T., De Raedt, L.: Correlated itemset mining in ROC space: a constraint programming approach. In: Proc. of the 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 647–656. ACM, New York (2009)
Hirao, M., Hoshino, H., Shinohara, A., Masayuki, T., Setsuo, A.: A practical algorithm to find the best subsequence patterns. In: Morishita, S., Arikawa, S. (eds.) DS 2000. LNCS (LNAI), vol. 1967, pp. 141–154. Springer, Heidelberg (2000)
Fischer, J., Mäkinen, V., Välimäki, N.: Space efficient string mining under frequency constraints. In: Proc. of the 8th Int. Conf. on Data Mining (ICDM 2008), pp. 193–202. IEEE Computer Society, Washington (2008)
Cuff, J.A., Barton, G.J.: Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 34(4), 508–519 (1999)
Kaur, H., Raghava, G.P.S.: An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508–1514 (2002)
Sonnhammer, E.L.L., von Heijne, G., Krogh, A.: A Hidden Markov Model for predicting transmembrane helices in protein sequences. In: Proc. of the 6th Int. Conf. on Intelligent Systems for Molecular Biology (ISMB 1998), pp. 175–182. AAAI Press, Menlo Park (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hopf, T., Kramer, S. (2010). Mining Class-Correlated Patterns for Sequence Labeling. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-16184-1_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16183-4
Online ISBN: 978-3-642-16184-1
eBook Packages: Computer ScienceComputer Science (R0)