Abstract
In recent years, early prediction for ongoing sequences has been more and more valuable in a large variety of time-critical applications which demand to classify an ongoing sequence in its early stage. There are two challenging issues in early prediction, i.e. why an ongoing sequence is early predictable and how to reasonably determine the parameter k optimal , the minimum number of elements that must be observed before an accurate classification can be made. To address these issues, this paper investigates the kinetic regularity of the information transfer in sequence data set. As a result, a new concept of Accumulatively Transferred Information (ATI) and its kinetic model in early predictable sequences are proposed. This model shows that the information transfer in early predictable sequences follows Inverse Heavy-tail Distribution(IHD), and the most uncertainty of an early predictable sequence is eliminated by only few of its preceding elements, which is exactly the intrinsic and theoretically sound ground of the feasibility of early prediction. Based on the kinetic model, a heuristic algorithm is proposed to learn the parameter k optimal . The experiments are conducted on real data sets and the results validate the reasonableness and effectiveness of the proposed theory and algorithm.
Supported by the Basic Research Foundation for Central Universities under Grant No. 2010SCU11053.
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
Dongand, G., Pei, J.: Sequence Data Mining. Springer, Heidelberg (2007)
Lesh, M.O.N., Zaki, M.J.: Mining features for sequence classification. In: Proc. of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 342–346. ACM, New York (1999)
Srikant, R.R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)
Pei, J., Han, J.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: Proc. of the 17th International Conference on Data Engineering, pp. 215–226. IEEE, Los Alamitos (2001)
Ayres, T.J., Flannick, J.: Sequential pattern mining using a bitmap representation. In: Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 429–435. ACM, New York (2002)
Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42(1), 31–60 (2001)
Cheng, J.H., Yan, X.: Seqindex: Indexing sequences by sequential pattern analysis. In: Proc. of 2005 SIAM International Conference on Data Mining, pp. 601–605. SIAM, Philadelphia (2005)
Parker, P.C., Fern, A.: Gradient boosting for sequence alignment. In: Proc. of the 21st National Conference on Artificial Intelligence, pp. 452–457 (2006)
Karwath, N.A.: Boosting relational sequence alignments. In: Proc. of the 8th IEEE International Conference on Data Mining, pp. 857–862. IEEE, Los Alamitos (2008)
Tseng, M.: Cbs: A new classification method by using sequential patterns. In: Proc. of 2005 SIAM International Conference on Data Mining, pp. 596–600. SIAM, Philadelphia (2005)
Exarchos, T.P., Tsipouras, M.G.: A two-stage methodology for sequence classification based on sequential pattern mining and optimization. Data and Knowledge Engineering 66(3), 467–487 (2008)
Wu, C., Berry, M.: Neural networks for full-scale protein sequence classification: Sequence encoding with singular value decomposition. Machine Learning 21(1), 177–193 (1995)
Ma, Q., Wang, J.T.L.: Dna sequence classication via an expectation maximization algorithm and neural networks: a case study. IEEE Transactions on Systems, Man and Cybernetics 31(4), 468–475 (2001)
Park, M.K.J.: Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 19(13), 1656–1663 (2003)
She, R., Chen, F.: Frequent-subsequence-based prediction of outer membrane proteins. In: Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 436–445. ACM, New York (2003)
Sonnenburg, S., Rätsch, G., Schäfer, C.: Learning interpretable sVMs for biological sequence classification. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 389–407. Springer, Heidelberg (2005)
Alonso, C.J., Rodriguez, J.J.: Boosting interval based literals: Variable length and early classification. In: Data Mining in Time Series Databases. World Scientific, Singapore (2004)
Xing, Z., Pei, J.: Mining sequence classifiers for early prediction. In: Proc. of 2008 SIAM International Conference on Data Mining, pp. 644–655. SIAM, Philadelphia (2008)
Cover, T.: Elements of Information Theory, 2nd edn. John Wiley, Chichester (2006)
Cohen, W.W., Singer, Y.: A simple, Fast, and Effective Learner. In: Proc. of the 16th National Conference on Artificial Intelligence, pp. 335–342 (1999)
Asuncion, A., Newman, D.J.: UCI machine learning repository (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, N., Peng, J., Chen, Y., Tang, C. (2011). Early Prediction of Temporal Sequences Based on Information Transfer. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 6897. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23535-1_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-23535-1_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23534-4
Online ISBN: 978-3-642-23535-1
eBook Packages: Computer ScienceComputer Science (R0)