No Access

FUZZY INTRINSIC MODE ENTROPY FOR DATA FEATURE EXTRACTION

GEORGE S. TSOLIS

Aristotles University of Thessaloniki, Thessaloniki, 54006, Greece

Search for more papers by this author

and

THOMAS D. XENOS

Aristotles University of Thessaloniki, Thessaloniki, 54006, Greece

Search for more papers by this author

https://doi.org/10.1142/S1793536912500094Cited by:0 (Source: Crossref)

Abstract

The notion of fuzzy entropy (FuzzyEn) is extended to the multiscale case by combining FuzzyEn and empirical mode decomposition (EMD). The proposed technique, fuzzy intrinsic entropy (FIMEn) performs better than its predecessor intrinsic monde entropy (IMEn) and it is less dependent on the algorithmic parameters. In a pattern recognition context, FIMEn provides more separable clusters than IMEn when used for feature extraction, thus allowing for less classification error. The above results suggest that the proposed multiscale entropy metric is a very promising technique for evaluating data regularity and can be used effectively for feature extraction in pattern recognition problems.

Keywords:

References

R. Alcaraz and J. J. Rieta, Med. Eng. Phys. 31(8), 917 (2009). Crossref, Google Scholar
H. Amoudet al., IEEE Signal Process. Lett. 14(5), 297 (2007). Crossref, Google Scholar
R. B. Calinski and J. Harabasz, Comm. Stat. 3, 1 (1974). Google Scholar
W. Chenet al., IEEE Trans. Neural Sys. Rehabil. Eng. 15(2), 266 (2007). Crossref, Google Scholar
W. Chenet al., Med. Eng. Phys. 31(1), 61 (2009). Crossref, Google Scholar
M. Costa, A. L. Goldberger and C. K. Peng, Phys. Rev. Lett. 89, 068102 (2002). Crossref, Google Scholar
D. L. Davies and D. W. Bouldin, IEEE Trans. Pattern Anal. Mach. Intelli. 1(2), 224 (1979). Google Scholar
R. P. Gorman and T. J. Sejnowski, Neural Netw. 1, 75 (1988). Crossref, Google Scholar
N. E. Huanget al., Proc. R. Soc. Lond. A 454, 903 (1998). Crossref, Google Scholar
L. Kaufman and P. Rousseeuw , Finding Groups in Data ( Wiley , New York , 1990 ) . Crossref, Google Scholar
V. E. Kosmidou and L. J. Hadjileontiadis, Intrinsic mode entropy: An enhanced classification means for automated Greek sign language gesture recognition, Engineering in Medicine and Biology Society, 2008, EMBS 2008. 30th Annual International Conference of the IEEE (2008) pp. 5057–5060. Google Scholar
M. J. Lewis and A. L. Short, Physiol. Meas. 28, 731 (2007). Crossref, Google Scholar
P. E. Pace , Detecting and Classifying Low Probability of Intercept Radar ( Artech House , Boston , 2004 ) . Google Scholar
S. M. Pincus, Proc. Nat. Acad. Sci. USA 88(6), 2297 (1991). Crossref, Google Scholar
J. S. Richman and J. R. Moorman, Am. J. Physiol. Heart Circ. Physiol. 278(6), H2039 (2000). Crossref, Google Scholar
F. Van der Heijden et al. , Classification, Parameter Estimation and State Estimation — An Engineering Approach Using Matlab ( John Wiley & Sons , Chichester , 2004 ) . Crossref, Google Scholar
G. Zhang, H. Rong and L. Hu, Entropy feature extraction approach for radar emitter signals, Proceedings International Conference on Intelligent Mechatronics and Automation (2004) pp. 621–625. Google Scholar