Page 260 - 《软件学报》2020年第10期
P. 260
3236 Journal of Software 软件学报 Vol.31, No.10, October 2020
[10] Hoppner F. Improving time series similarity measures by integrating preprocessing steps. Data Mining and Knowledge Discovery,
2017,31(3):851–878. [doi: 10.1007/s10618-016-0490-x]
[11] Yuan JD, Douzal-Chouakria A, Yazdi SV, Wang ZH. A large margin time series nearest neighbour classification under locally
weighted time warps. Knowledge and Information Systems, 2018,59(1):117−135. [doi: 10.1007/s10115-018-1184-z]
[12] Yuan JD, Wang ZH, Han M. Shapelet pruning and shapelet coverage for time series classification. Ruan Jian Xue Bao/Journal of
Software, 2015,26(9):2311–2325 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4702.htm [doi: 10.13328/j.
cnki.jos.004702]
[13] Yuan JD, Wang ZH, Han M, You Y. A logical shapelets transformation for time series classification. Chinese Journal of Computers,
2015,38(7):1448–1459 (in Chinese with English abstract). [doi: 0.11897/SP.J.1016.2015.01448]
[14] Neuyen TL, Gsponer S, Ifrim G. Time series classification by sequence learning in all-subsequence space. In: Proc. of the 33th Int’l
Conf. on Data Engineering (ICDE 2017). San Diego: IEEE, 2017. 947–958. [doi: 10.1109/ICDE.2017.142]
[15] Shi M, Wang Z, Yuan J, Liu H. Random pairwise shapelets forest. In: Proc. of the 22th Pacific-Asia Conf. on Knowledge Discovery
and Data Mining (PAKDD 2018). Cham: Springer-Verlag, 2018. 68–80. [doi: 10.1007/978-3-319-93034-3_6]
[16] Bostrom A, Bagnall A. Binary shapelet transform for multiclass time series classification. In: Hameurlain A, ed. Trans. on
Large-scale Data- and Knowledge-centered Systems XXXII. LNCS 10420, Berlin: Springer-Verlag, 2017. 24–46. [doi: 10.1007/
978-3-662-55608-5_2]
[17] Bagnall A, Lines J, Hills J, Bostrom A. Time-series classification with COTE: The collective of transformation-based ensembles.
IEEE Trans. on Knowledge and Data Engineering, 2015,27(9):2522–2535. [doi: 10.1109/TKDE.2015.2416723]
[18] Lin J, Keogh E, Li W, Lonardi S. Experiencing SAX: A novel symbolic representation of time series. Data Mining and Knowledge
Discovery, 2007,15(2):107–144. [doi: 10.1007/s10618-007-0064-z]
[19] Senin P, Malinchik S. SAX-VSM: Interpretable time series classification using SAX and vector space model. In: Proc. of the 13th
IEEE Int’l Conf. on Data Mining (ICDM2013). Dallas: IEEE, 2013. 1175−1180. [doi: 10.1109/ICDM.2013.52]
[20] Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases. In: Proc. of the 4th Int’l Conf. on Foundations
of Data Organization and Algorithms. Berlin, Heidelberg: Springer-Verlag, 1993. 69–84. [doi: 10.1007/3-540-57301-1_5]
[21] Rafiei D, Mendelzon A. Efficient retrieval of similar time sequences using DFT. In: Proc. of the 5th Int’l Conf. of Foundations of
Data Organization (FODO 1998). Kobe: IEEE, 1998. 249–257. [doi: 10.1109/icde.1998.655778]
[22] Schafer P, Högqvist M. SFA: A symbolic Fourier approximation and index for similarity search in high dimensional datasets. In:
Proc. of the 15th Int’l Conf. on Extending Database Technology. Berlin: ACM, 2012. 516–527. [doi: 10.1145/2247596.2247656]
[23] Schafer P. The BOSS is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery,
2015,29(6):1505–1530. [doi: 10.1007/s10618-014-0377-7]
[24] Schafer P, Leser U. Fast and accurate time series classification with WEASEL. In: Proc. of the 26th ACM Int’l Conf. on
Information and Knowledge Management (CIKM 2017). ACM, 2017. 637–646. [doi: 10.1145/3132847.3132980]
[25] Oppenheim AV, Schafer RW. Digital Signal Processing. Englewood Cliffs: Prentice Hall, 1975.
[26] Erra U, Senatore S, Minnella F, Caggianese G. Approximate TF-IDF based on topic extraction from massive message stream using
the GPU. Information Sciences, 2015,292:143–161. [doi: 10.1016/j.ins.2014.08.062]
[27] Chen K, Zhang Z, Long J, Zhang H. Turning from TF-IDF to TF-IGM for term weighting in text classification. Expert Systems with
Applications, 2016,66:245–260. [doi: 10.1016/j.eswa.2016.09.009]
[28] Schafer P. Scalable time series classification. Data Mining and Knowledge Discovery, 2016,30(5):1273–1298. [doi: 10.1007/
s10618-015-0441-y]
[29] Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ. LIBLINEAR: A library for large linear classification. Journal of Machine
Learning Research, 2008,9:1871–1874.
[30] Bagnall A, Lines J, Vickers W, Keogh E. The UEA & UCR time series classification repository. http://www.timeseriesclassification.
com
[31] Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 2006,7(1):1–30.
[32] Rakthanmanon T, Keogh E. Fast shapelets: A scalable algorithm for discovering time series shapelets. In: Proc. of the 13th SIAM
Int’l Conf. on Data Mining. Austin: SIAM Press, 2013. 668–676. [doi: 10.1137/1.9781611972832.74]