Computer Science ›› 2018, Vol. 45 ›› Issue (5): 180-184.doi: 10.11896/j.issn.1002-137X.2018.05.030
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ZHAO Hui-yun and PAN Zhi-song
[1] YUAN J D,WANG Z H.Review of Time Series Representation and Classification Techniques[J].Computer Science,2015,42(3):1-7.(in Chinese) 原继东,王志海.时间序列的表示与分类算法综述[J].计算机科学,2015,42(3):1-7. [2] LI Z X,ZHANG F M,ZHANG X F,et al.Survey of similarity search for multivariate time series[J].Control and Decision,2017,32(4):577-583.(in Chinese) 李正欣,张凤鸣,张晓丰,等.多元时间序列相似性搜索研究综述[J].控制与决策,2017,32(4):577-583. [3] WANG L,WANG Z,LIU S.An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm[J].Expert Systems with Applications,2016,43(C):237-249. [4] DING H,TRAJCEVSKI G,SCHEUERMANN P,et al.Querying and mining of time series data:experimental comparison of representations and distance measures[J].Proceedings of the VLDB Endowment,2008,1(2):1542-1552. [5] YE L,KEOGH E.Time series shapelets:a new primitive for data mining[C]∥Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2009:947-956. [6] HILLS J,LINES J,BARANAUSKAS E,et al.Classification of time series by shapelet transformation[J].Data Mining and Knowledge Discovery,2014,28(4):851-881. [7] GRABOCKA J,SCHILLING N,WISTUBA M,et al.Learning time-series shapelets[C]∥Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2014:392-401. [8] HOU L,ZURADA J M,ZURADA J M.Efficient learning oftimeseries shapelets[C]∥Thirtieth AAAI Conference on Artificial Intelligence.AAAI Press,2016:1209-1215. [9] MUEEN A,KEOGH E,YOUNG N.Logical-shapelets:an expressive primitive for time series classification[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2011:1154-1162. [10] WISTUBA M,GRABOCKA J,SCHMIDT-THIEME L.Ultra-fast shapelets for time series classification[J].arXiv preprint arXiv:1503.05018,2015. [11] LIU J,YUAN L,YE J.An efficient algorithm for a class of fused lasso problems[C]∥ACM SIGKDD International Confe-rence on Knowledge Discovery and Data Mining.ACM,2010:323-332. [12] YOON H,YANG K,SHAHABI C.Feature subset selection and feature ranking for multivariate time series[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(9):1186-1198. [13] LI C,KHAN L,PRABHAKARAN B.Real-time classification of variable length multi-attribute motions[J].Knowledge and Information Systems,2006,10(2):163-183. [14] LI C,KHAN L,PRABHAKARAN B.Feature Selection forClassification of Variable Length Multiattribute Motions[M].Springer-Verlag New York,Inc.2006. [15] HE G,DUAN Y,PENG R,et al.Early classification on multivariate time series[J].Neurocomputing,2015,149(PB):777-787. [16] LI H.Accurate and efficient classification based on commonprincipal components analysis for multivariate time series[J].Neurocomputing,2016,171(C):744-753. [17] GRECKI T,UCZAK M.Multivariate time series classification with parametric derivative dynamic time warping[J].Expert Systems with Applications,2015,42(5):2305-2312. [18] MUEEN A,KEOGH E,YOUNG N.Logical-shapelets:an expressive primitive for time series classification[C]∥Proceedings of the 17th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining.ACM,2011:1154-1162. [19] GHALWASH M F,OBRADOVIC Z.Early classification of mul-tivariate temporal observations by extraction of interpretable shapelets[J].BMC Bioinformatics,2012,13(1):195. [20] RAKTHANMANON T,KEOGH E.Fast shapelets:A scalable algorithm for discovering time series shapelets[C]∥Proceedings of the 2013 SIAM International Conference on Data Mining.Society for Industrial and Applied Mathematics,2013:668-676. [21] DIETTERICH T G,KONG E B.Machine learning bias,statistical bias,and statistical variance of decision tree algorithms[R].Technical report,Department of Computer Science,Oregon State University,1995. [22] FRIEDMAN J,HASTIE T,HFLING H,et al.Pathwise coordinate optimization[J].Annals of Applied Statistics,2007,1(2):302-332. [23] ROSE D J.An algorithm for solving a special class of tridiagonal systems of linear equations[J].Communications of the Acm,1969,12(12):234-236. [24] BAUER E,KOHAVI R.An empirical comparison of voting classi-fication algorithms:Bagging,boosting,and variants[J].Machine Learning,1999,36(1):105-139. |
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