Class Progression Phase Sequence Grouping with Cooperative Transformation Centered Ensembles

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In recent times, dualistic notions have been discovered that lead to more precise procedures for Phase - Sequence Grouping (PSG). Initially, it remained exposed that the artless method to increase progress on PSG complications is to renovate into an alternate information space wherever biased features are certainly noticed. Succeeding proof of an individual information depiction, enriched correctness that can be attained over artless cooperative patterns. These dual ideologies are associated to assess the premise that bring into existence a cooperative groups of classifiers on dissimilar information renovations to progress the correctness of PSG through Class Progression Phase Sequence Grouping (CPPSG). For the phase area, a set of flexible remoteness trials are used. The artless cooperative pattern is demonstrated by comprising all classifiers in single cooperative pattern is meaningfully more precise than any of its mechanisms and to some extent supplementary methods available in earlier Time-series Classifier procedures.

     

     


  • Keywords


    Phase - Sequence Grouping, correctness, cooperative pattern, classifiers, information.

  • References


      [1] E. Keogh and T. Folias, The UCR time series data mining archive. (2015). [Online]. Available: http://www.cs.ucr.edu/ eamonn/TSDMA/

      [2] J. Lin, R. Khade, and Y. Li, “Rotation-invariant similarity in time series using bag-of-patterns representation,” J. Intell. Inf. Syst., vol. 39, no. 2, pp. 287–315, 2012.

      [3] M. Baydogan, G. Runger, and E. Tuv, “A bag-of-features framework to classify time series,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 11, pp. 2796–2802, Jun. 2013.

      [4] Stefan, V. Athitsos, and G. Das, “The move-split-merge metric for time series,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 6, pp. 1425–1438, Jun. 2013.

      [5] H. Deng, G. Runger, E. Tuv, and M. Vladimir, “A time series forest for classification and feature extraction,” Inf. Sci., vol. 239, pp. 142–153, 2013.

      [6] T. Rakthanmanon and E. Keogh, “Fast-shapelets: A fast algorithm for discovering robust time series shapelets,” in Proc. 13th SDM, 2013, pp. 668–676.

      [7] G. Batista, X. Wang, and E. Keogh, “A complexity-invariant distance measure for time series,” Data Mining Knowl. Discovery, vol. 28, no. 3, pp. 634–669, 2013.

      [8] Y. Jeong, M. Jeong, and O. Omitaomu, “Weighted dynamic time warping for time series classification,” Pattern Recognit., vol. 44, pp. 2231–2240, 2011.

      [9] P. Marteau, “Time warp edit distance with stiffness adjustment for time series matching,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 306–318, Feb. 2009.

      [10] Ratanamahatana and E. Keogh, “Three myths about dynamic time warping,” in Proc. 10th SDM, 2005, pp. 506–510.

      [11] H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, “Querying and mining of time series data: Experimental comparison of representations and distance measures,” Proc. 34th VLDB Endowment, vol. 1, pp. 1542–1552, 2008.

      [12] G. B. D. Silva, V. de Souza, “Time series classification using compression distance of recurrence plots,” in Proc. IEEE 13th Int. Conf. Data Mining, 2013, pp. 687–696.

      [13] Fulcher and N. Jones, “Highly comparative feature-based timeseries classification,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 12, pp. 3026–3037, 2014.

      [14] L. Ye and E. Keogh, “Time series shapelets: A new primitive for data mining,” in Proc. 15th ACM Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 947–956.

      [15] J. Lines, L. Davis, J. Hills, and A. Bagnall, “A shapelet transform for time series classification,” in Proc. 18th ACM Int. Conf. Knowl. Discovery Data Mining, 2012, pp. 289–297.

      [16] Bagnall, Time series classification website. (2015). [Online]. Available: http://www.uea.ac.uk/computing/tsc.

      [17] J. Dem


      sar, “Statistical comparisons of classifiers over multiple data sets,” J. Mach. Learning Res., vol. 7, pp. 1–30, 2006.

      [18] J. Hills, J. Lines, E. Baranauskas, J. Mapp, and A. Bagnall, “Classification of time series by shapelet transformation,” Data Mining Knowl. Discovery, vol. 28, pp. 851–881, 2014.

      [19] Bagnall, L. Davis, J. Hills, and J. Lines, “Transformation based ensembles for time series classification,” in Proc. 12th SDM, 2012, vol. 12, pp. 307–318.

      [20] M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, “Mining data streams: A review,” SIGMOD Rec., vol. 34, no. 2, pp. 18–26, 2005.


 

View

Download

Article ID: 22968
 
DOI: 10.14419/ijet.v7i3.20.22968




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.