Abstract
A chronicle is a kind of temporal pattern mined from a set of sequences made-up of time-stamped events. It has been shown recently that such knowledge is effective in sketching machines’ behaviours in industry. However, chronicles that describe a same new sequence of events could be multiple and conflictual. To predict nature and time interval of future events, we need to consider all the chronicles that match a new sequence. In this paper, we introduce a new approach, called FCP, that uses the evidence theory and chronicle mining to classify sequences. The approach has been evaluated on both synthetic and real-world data sets and compared to baseline state-of-the-art approaches.
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Notes
- 1.
Reader may refer to https://gitlab.inria.fr/tguyet/pychronicles for further details about data sets generation.
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Acknowledgements
This work has received funding from INTERREG Upper Rhine (European Regional Development Fund) and the Ministries for Research of Baden- Wrttemberg, Rheinland-Pfalz (Germany) and from the Grand Est French Region in the framework of the Science Offensive Upper Rhine HALFBACK project.
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Ben Chrayet, A., Samet, A., Bach Tobji, M.A. (2020). Evidence Theory Based Combination of Frequent Chronicles for Failure Prediction. In: Davis, J., Tabia, K. (eds) Scalable Uncertainty Management. SUM 2020. Lecture Notes in Computer Science(), vol 12322. Springer, Cham. https://doi.org/10.1007/978-3-030-58449-8_16
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