Abstract
We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS’s performance.
This research was supported by funding from ISM Canada and the Natural Sciences and Engineering Research Council of Canada.
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
Similar content being viewed by others
References
Sheetrit, E., Nissim, N., Klimov, D., Shahar, Y.: Temporal probabilistic profiles for sepsis prediction in the ICU. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2961–2969. ACM (2019)
Patel, D., Hsu, W., Lee, M.L.: Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, pp. 393–404. ACM, New York (2008)
Moskovitch, R., Shahar, Y.: Medical temporal-knowledge discovery via temporal abstraction. In: AMIA Annual Symposium Proceedings, pp. 452–456. American Medical Informatics Association (2009)
Mörchen, F., Fradkin, D.: Robust mining of time intervals with semi-interval partial order patterns. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 315–326. SIAM (2010)
Papapetrou, P., Kollios, G., Sclaroff, S., Gunopulos, D.: Mining frequent arrangements of temporal intervals. Knowl. Inf. Syst. 21(2), 133 (2009)
Liu, Y., Nie, L., Liu, L., Rosenblum, D.S.: From action to activity: sensor-based activity recognition. Neurocomputing 181, 108–115 (2016)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107–144 (2007)
Mörchen, F., Ultsch, A.: Optimizing time series discretization for knowledge discovery. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 660–665. ACM (2005)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Workshop, K.D.D. (ed.) Seattle, pp. 359–370. AAAI Press, WA (1994)
Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A temporal pattern mining approach for classifying electronic health record data. ACM Trans. Intell. Syste. Technol. (TIST) 4(4), 63 (2013)
Moskovitch, R., Shahar, Y.: Classification-driven temporal discretization of multivariate time series. Data Min. Knowl. Disc. 29(4), 871–913 (2014). https://doi.org/10.1007/s10618-014-0380-z
Kostakis, O., Papapetrou, P., Hollmén, J.: ARTEMIS: assessing the similarity of event-interval sequences. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS (LNAI), vol. 6912, pp. 229–244. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23783-6_15
Kotsifakos, A., Papapetrou, P., Athitsos, V.: IBSM: interval-based sequence matching. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 596–604. SIAM (2013)
Bornemann, L., Lecerf, J., Papapetrou, P.: STIFE: a framework for feature-based classification of sequences of temporal intervals. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 85–100. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_6
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM (2009)
Tang, J., Alelyani, S., Liu, H.: Feature selection for classification: a review. Algorithms and applications, Data classification, p. 37 (2014)
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Machine Learning Proceedings 1994, pp. 121–129. Elsevier (1994)
Yu, L., Liu, H.: Redundancy based feature selection for microarray data. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 737–742. ACM (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohammad Mirbagheri, S., Hamilton, H.J. (2020). FIBS: A Generic Framework for Classifying Interval-Based Temporal Sequences. In: Song, M., Song, IY., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2020. Lecture Notes in Computer Science(), vol 12393. Springer, Cham. https://doi.org/10.1007/978-3-030-59065-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-59065-9_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59064-2
Online ISBN: 978-3-030-59065-9
eBook Packages: Computer ScienceComputer Science (R0)