Abstract
Behavior analysis received much attention in recent year, such as customer-relationship management, social security surveillance and e-business. Discovering high impact-driven behavior patterns is important for detecting and preventing their occurrences and reducing resulting risks and losses to our society. In data mining community, researchers pay little attention to time-stamps in temporal behavior sequences (without explicitly considering inherent temporal information) during classification. In this paper, we propose a novel Temporal Feature Extraction Method - TFEM. It extracts sequential pattern features where each transition is annotated with a typical transition time (its duration or interval). Therefore it substantially enriches temporal characteristics derived from temporal sequences, yielding improvements in performances, as demonstrated by a set of experiments performed on synthetic and real-world datasets. In addition, TFEM has the merit of simplicity in implementation and its pattern-based architecture can generate human-readable results and supply clear interpretability to users. Meanwhile, it is adjustable and adaptive to user’s different configurations, allowing a tradeoff between classification accuracy and time cost.
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
Foxall, C., James, V.: Behavior Analysis of Consumer Brand Choice: A Preliminary Analysis1. The Behavioral Economics of Brand Choice, p. 54 (2007)
Cao, L.: Behavior informatics and analytics: Let behavior talk. In: ICDM Workshops, pp. 87–96. IEEE Computer Society, Los Alamitos (2008)
Lesh, N., Zaki, M.J., Ogihara, M.: Mining features for sequence classification. In: KDD 1999: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 342–346. ACM, New York (1999)
Brigham, E., Yuen, C.: The fast Fourier transform. IEEE Transactions on Systems, Man and Cybernetics 8(2), 146–146 (1978)
Golub, G., Reinsch, C.: Singular value decomposition and least squares solutions. Numerische Mathematik 14(5), 403–420 (1970)
Eriksson, K., Estep, D., Hansbo, P., Johnson, C.: Introduction to adaptive methods for differential equations. Acta numerica 4, 105–158 (2008)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15(2), 107–144 (2007)
Lesh, N., Zaki, M., Ogihara, M.: Mining features for sequence classification. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 342–346. ACM, New York (1999)
Zaki, M.: SPADE: An efficient algorithm for mining frequent sequences. Machine Learning 42(1), 31–60 (2001)
Ma, Q., Wang, J., Shasha, D., Wu, C.: DNA sequence classification via an expectation maximization algorithm and neural networks: a case study. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 31(4), 468–475 (2001)
Rätsch, G., Sonnenburg, S., Schäfer, C.: Learning interpretable SVMs for biological sequence classification. BMC bioinformatics 7(Suppl. 1), S9 (2006)
Ferreira, P., Azevedo, P.: Protein sequence classification through relevant sequence mining and bayes classifiers. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, pp. 236–247. Springer, Heidelberg (2005)
Mulder, N., Apweiler, R.: InterPro and InterProScan: tools for protein sequence classification and comparison. Methods in Molecular Biology (Clifton, NJ)Â 396, 59 (2007)
Shen, L., Satta, G., Joshi, A.: Guided learning for bidirectional sequence classification. In: Annual Meeting-Association for Computational Linguistics, vol. 45, p. 760 (2007)
Spurdle, A., Lakhani, S., Healey, S., Parry, S., Da Silva, L., Brinkworth, R., Hopper, J., Brown, M., Babikyan, D., Chenevix-Trench, G., et al.: Clinical classification of BRCA1 and BRCA2 DNA sequence variants: the value of cytokeratin profiles and evolutionary analysis–a report from the kConFab Investigators. Journal of Clinical Oncology 26(10), 1657 (2008)
Atalay, V., Cetin-Atalay, R.: Implicit motif distribution based hybrid computational kernel for sequence classification. Bioinformatics 21(8), 1429–1436 (2005)
Quinlan, J.: Learning logical definitions from relations. Machine learning 5(3), 239–266 (1990)
Uci kdd repository, http://archive.ics.uci.edu/ml/datasets/Ionosphere:
Jolliffe, I.: Principal component analysis. Springer, Heidelberg (2002)
Gorban, A., Kgl, B., Wunsch, D., Zinovyev, A.: Principal manifolds for data visualization and dimension reduction, p. 340. Springer Publishing Company, Heidelberg (2007) (incorporated)
Rohlf, F.: Morphometric spaces, shape components and the effects of linear transformations. In: Advances in morphometrics, pp. 117–129 (1996)
Cai, D., He, X., Han, J., Zhang, H.: Orthogonal laplacianfaces for face recognition. IEEE Transactions on Image Processing 15(11), 3608–3614 (2006)
Keogh, E., Pazzani, M.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM, New York (2000)
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
Yang, Y., Cao, L., Liu, L. (2010). Time-Sensitive Feature Mining for Temporal Sequence Classification. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-15246-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15245-0
Online ISBN: 978-3-642-15246-7
eBook Packages: Computer ScienceComputer Science (R0)