An Incremental Change Detection Test Based on Density Difference Estimation

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Volumn 47, Issue 10, 2017, Pages 2714-2726

  Bu, Li ^a   Zhao, Dongbin ^a   Alippi, Cesare ^b,c  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b POLITECNICO DI MILANO   (Italy)

^c UNIVERSITY OF LUGANO   (Switzerland)

Author keywords

Change detection; incremental computing; incremental least squares density difference change detection method (LSDD Inc); probability density function (pdf) free

Indexed keywords

BIG DATA; LEAST SQUARES APPROXIMATIONS; TESTING;

CHANGE DETECTION; DENSITY DIFFERENCE; FALSE POSITIVE RATES; INCREMENTAL COMPUTING; LINEAR COMBINATIONS; PROBABILITY DENSITY FUNCTION (PDF); PROBABILITY DENSITY FUNCTIONS (PDFS); THEORETICAL FRAMEWORK;

PROBABILITY DENSITY FUNCTION;

EID: 85030027570     PISSN: 21682216     EISSN: 21682232     Source Type: Journal    
DOI: 10.1109/TSMC.2017.2682502     Document Type: Article

References (39)

1. H. Liu, F. Sun, D. Guo, B. Fang, and Z. Peng, "Structured output-associated dictionary learning for haptic understanding," IEEE Trans. Syst., Man, Cybern., Syst., to be published, doi: 10.1109/TSMC.2016.2635141.
2. H. Liu, J. Qin, F. Sun, and D. Guo, "Extreme kernel sparse learning for tactile object recognition," IEEE Trans. Cybern., to be published, doi: 10.1109/TCYB.2016.2614809.
3. H. Liu, D. Guo, and F. Sun, "Object recognition using tactile measurements: Kernel sparse coding methods," IEEE Trans. Instrum. Meas., vol. 65, no. 3, pp. 656-665, Mar. 2016.
4. H. Liu, Y. Liu, and F. Sun, "Robust exemplar extraction using structured sparse coding," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 8, pp. 1816-1821, Aug. 2015.
5. Q. Zhang, D. Zhao, and Y. Zhu, "Event-triggered H∞ control for continuous-time nonlinear system via concurrent learning," IEEE Trans. Syst., Man, Cybern., Syst., to be published, doi: 10.1109/TSMC.2016.2531680.
6. Q. Zhang, D. Zhao, and D. Wang, "Event-based robust control for uncertain nonlinear systems using adaptive dynamic programming," IEEE Trans. Neural Netw. Learn. Syst., to be published, doi: 10.1109/TNNLS.2016.2614002.
7. C. Alippi and M. Roveri, "Just-in-time adaptive classifiers-Part I: Detecting nonstationary changes," IEEE Trans. Neural Netw., vol. 19, no. 7, pp. 1145-1153, Jul. 2008.
8. C. Alippi, G. Boracchi, and M. Roveri, "Just in time classifiers: Managing the slow drift case," in Proc. IEEE Int. Joint Conf. Neural Netw., Atlanta, GA, USA, 2009, pp. 114-120.
9. D. M. Hawkins and K. Zamba, "Statistical process control for shifts in mean or variance using a changepoint formulation," Technometrics, vol. 47, no. 2, pp. 164-173, 2005.
10. G. J. Ross, D. K. Tasoulis, and N. M. Adams, "Nonparametric monitoring of data streams for changes in location and scale," Technometrics, vol. 53, no. 4, pp. 379-389, 2011.
11. G. J. Ross and N. M. Adams, "Two nonparametric control charts for detecting arbitrary distribution changes," J. Qual. Technol., vol. 44, no. 2, pp. 102-116, 2012.
12. D. M. Hawkins, P. Qiu, and C. W. Kang, "The changepoint model for statistical process control," J. Qual. Technol., vol. 35, no. 4, pp. 355-366, 2003.
13. Y. Lepage, "Combination of Wilcoxians and Ansari-Bradley statistics," Biometrika, vol. 58, no. 1, pp. 213-217, Apr. 1971.
14. M. F. Schilling, "Multivariate two-sample tests based on nearest neighbors," J. Amer. Stat. Assoc., vol. 81, no. 395, pp. 799-806, 1986.
15. S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, "Online outlier detection in sensor data using nonparametric models," in Proc. Int. Conf. Very Large Data Bases (VLDB), Seoul, South Korea, 2006, pp. 187-198.
16. R. Sebastião, J. Gama, P. P. Rodrigues, and J. Bernardes, "Monitoring incremental histogram distribution for change detection in data streams," in Proc. 2nd Int. Conf. Knowl. Discov. Sensor Data, Las Vegas, NV, USA, 2010, pp. 25-42.
17. T. Dasu, S. Krishnan, S. Venkatasubramanian, and K. Yi, "An information-theoretic approach to detecting changes in multi-dimensional data streams," in Proc. Symp. Interface Stat. Comput. Sci. Appl., vol. 67. Pasadena, CA, USA, 2006, pp. 1-21.
18. Q. Xu and L. J. Karam, "Change detection on SAR images by a parametric estimation of the KL-divergence between Gaussian mixture models," in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., Vancouver, BC, Canada, 2013, pp. 2109-2113.
19. G. Ditzler and R. Polikar, "Hellinger distance based drift detection for nonstationary environments," in Proc. IEEE Symp. Comput. Intell. Dyn. Uncertain Environ., Paris, France, 2011, pp. 41-48.
20. C. Alippi, G. Boracchi, D. Carrera, and M. Roveri, "Change detection in multivariate datastreams: Likelihood and detectability loss," unpublished paper, 2015. [Online]. Available: https://arxiv.org/abs/1510.04850
21. S. Liu, M. Yamada, N. Collier, and M. Sugiyama, "Change-point detection in time-series data by relative density-ratio estimation," Neural Netw., vol. 43, pp. 72-83, Jul. 2013.
22. M. Sugiyama et al., "Density-difference estimation," Neural Comput., vol. 25, no. 10, pp. 2734-2775, 2013.
23. L. Bu, C. Alippi, and D. Zhao, "A pdf-free change detection test based on density difference estimation," IEEE Trans. Neural Netw. Learn. Syst., to be published, doi: 10.1109/TNNLS.2016.2619909.
24. L. Bu, C. Alippi, and D. Zhao, "Ensemble LSDD-based change detection tests," in Proc. IEEE Int. Joint Conf. Neural Netw., Vancouver, BC, Canada, 2016, pp. 4064-4069.
25. C. Alippi, G. Boracchi, and M. Roveri, "A just-in-time adaptive classification system based on the intersection of confidence intervals rule," Neural Netw., vol. 24, no. 8, pp. 791-800, 2011.
26. A. C. Davison and D. V. Hinkley, Bootstrap Methods and Their Application, vol. 1. Cambridge, U.K.: Cambridge Univ. Press, 1997.
27. J. S. Vitter, "Random sampling with a reservoir," ACM Trans. Math. Softw., vol. 11, no. 1, pp. 37-57, 1985.
28. H. Raza, G. Prasad, and Y. Li, "EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments," Pattern Recognit., vol. 48, no. 3, pp. 659-669, 2015.
29. G. Ditzler and R. Polikar, "Hellinger distance based drift detection for nonstationary environments," in Proc. IEEE Symp. Comput. Intell. Dyn. Uncertain Environ., Paris, France, 2011, pp. 41-48.
30. L. L. Minku, A. P. White, and X. Yao, "The impact of diversity on online ensemble learning in the presence of concept drift," IEEE Trans. Knowl. Data Eng., vol. 22, no. 5, pp. 730-742, May 2010.
31. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Mach. Learn., vol. 23, no. 1, pp. 69-101, 1996.
32. P. Tüfekci, "Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods," Int. J. Elect. Power Energy Syst., vol. 60, pp. 126-140, Sep. 2014.
33. H. Kaya, P. Tüfekci, and F. S. Gürgen, "Local and global learning methods for predicting power of a combined gas & steam turbine," in Proc. Int. Conf. Emerg. Trends Comput. Electron. Eng., Dubai, UAE, 2012, pp. 13-18.
34. C. Alippi, D. Liu, D. Zhao, and L. Bu, "Detecting and reacting to changes in sensing units: The active classifier case," IEEE Trans. Syst., Man, Cybern., Syst., vol. 44, no. 3, pp. 353-362, Mar. 2014.
35. C. Alippi, G. Boracchi, and M. Roveri, "Change detection tests using the ICI rule," in Proc. Int. Joint Conf. Neural Netw., Barcelona, Spain, 2010, pp. 1-7.
36. A. Gretton et al., "Optimal kernel choice for large-scale two-sample tests," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 1205-1213.
37. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learn. Res., vol. 7, pp. 1-30, Jan. 2006.
38. C. McDiarmid, "On the method of bounded differences," Surveys Comb., vol. 141, no. 1, pp. 148-188, 1989.
39. K. Sridharan, "A gentle introduction to concentration inequalities," Dept. Comput. Sci., Cornell Univ., Tech. Rep., 2002. [Online]. Available: http://www.cs.cornell.edu/sridharan/concentration.pdf