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Local Classifiers as a Method of Analysing and Classifying Signals

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Applications of Computational Intelligence in Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 122))

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Summary

Biological sciences very often deal with data that are measured in consecutive time periods. Typical examples are EEG, ECG. In mathematical language such data are called signals. Classical methods of analysing and classifying data, like decision trees, are not suitable for signals as they ignore time nature of data.

We propose a novel method, called Local Classifiers, for analysing and classifying signals. The method is a genuine combination of wavelets and Support Vector Machines. On a referential data sets the method proved to be competitive as far as accuracy is concerned to other state-of-the-art methods. We also presented an application of the method to biological data set. The goal of the experiment was to study whether habituated and aroused states can be differentiated in single barrel column of rat’s somatosensory cortex by means of analysis of field potentials evoked by stimulation of a single vibrissa. The method proved to be a reliable approach to automatically detection of important parts of local field potentials as far as discrimination between two states of a brain are concerned. The results confirmed previous biological hypothesis.

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References

  1. Sweldens W (1998) The lifting scheme: A construction of second generation wavelets, SIAM Journal on Mathematical Analysis 29(2):511–546

    Article  MATH  MathSciNet  Google Scholar 

  2. R Claypoole RN R Baraniuk (1998) Adaptive wavelet transforms via lifting, In: Transactions of the International Conference on Acoustics, Speech and Signal Processing, 1513–1516

    Google Scholar 

  3. Daubechies I (1992) Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA

    MATH  Google Scholar 

  4. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA

    MATH  Google Scholar 

  5. Breiman L (1998) Arcing classifiers, The Annals of Statistics 26(3):801–849

    Article  MATH  MathSciNet  Google Scholar 

  6. Gonzalez CA, Diez JJR (2000) Time series classification by boosting interval based literals, Inteligencia Artificial, Revista Iberoamericana de Inteligencia Arti ficial 11:2–11

    Google Scholar 

  7. Rodríguez JJ, Alonso CJ (2004) Interval and dynamic time warping-based decision trees, In: SAC '04: Proceedings of the 2004 ACM symposium on Applied computing, 548–552. ACM Press, New York, NY, USA

    Chapter  Google Scholar 

  8. Rodríguez JJ, Alonso CJ, Maestro JA (2005) Support vector machines of interval-based features for time series classification., Knowl-Based Syst 18(4-5):171–178

    Article  Google Scholar 

  9. Saito N (1994) Local Feature Extraction and Its Application Using a Library of Bases. Ph.D. Thesis, Yale University

    Google Scholar 

  10. Kadous MW (2002) Temporal Classification: Extending the Classification Paradigm to Multivariate Time Series. Ph.D. Thesis, School of Computer Science & Engineering, University of New South Wales

    Google Scholar 

  11. Berndt D, Clifiord J (1994) Using dynamic time warping to find patterns in time series, In: AAAI Workshop on Knowledge Discovery in Databases, 229–248

    Google Scholar 

  12. Ratanamahatana C, Keogh EJ (2004) Making Time-Series Classification More Accurate Using Learned Constraints., In: Berry MW, Dayal U, Kamath C, Skillicorn DB (eds.) SDM. SIAM

    Google Scholar 

  13. Keogh EJ, Pazzani MJ (1999) Scaling up Dynamic Time Warping to Massive Dataset., In: PKDD, vol. 1704 of Lecture Notes in Computer Science, 1–11. Springer

    Google Scholar 

  14. Olszewski RT (2001) Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. Ph.D. Thesis, Carnegie Mellon University

    Google Scholar 

  15. Hastie T, Tibshirani R, Friedman JH (2001) The Elements of Statistical Learning. Springer

    Google Scholar 

  16. Vapnik VN (1998) Statistical Learning Theory. John Wiley & Sons

    Google Scholar 

  17. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers, In: Knowledge Discovery and Data Mining, 77–86

    Google Scholar 

  18. Schapire RE (1997) Using output codes to boost multiclass learning problems, In: Proc. 14th International Conference on Machine Learning, 313–321. Morgan Kaufmann

    Google Scholar 

  19. Wolpert DH (1990) Stacked generalization. Tech. Rep. LA-UR-90-3460, Los Alamos, NM

    Google Scholar 

  20. Breiman L, et al. (1984) Classification and Regression Trees. Chapman & Hall, New York

    MATH  Google Scholar 

  21. Alcock RJ, Manolopoulos Y (1999) Time-Series Similarity Queries Employing a Feature-Based Approach, In: Proceedings 7th Panhellenic Conference in Informatics (PCI'99), III.1–9

    Google Scholar 

  22. Geurts P (2002) Contributions to decision tree induction: bias/variance tradeofi and time series classification. Ph.D. Thesis, University of Liége, Belgium

    Google Scholar 

  23. Roverso D (2000). Multivariate temporal classification by windowed wavelet decomposition and recurrent neural networks

    Google Scholar 

  24. Kudo M, Toyama J, Shimbo M (1999). Multidimensional curve classification using passing-through regions

    Google Scholar 

  25. Alimoglu F (1996) Combining Multiple Classifiers for Pen-Based Handwritten Digit Recognition. MA Thesis, Institute of Graduate Studies in Science and Engineering, Bogazici University

    Google Scholar 

  26. Cun YL, Boser B, Denker JS, Henderson D, Howard RE, Howard W, Jackel LD (1990) In: Advances in Neural Information Processing Systems II, 396–404. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  27. Geurts P,Wehenkel L (2005) Segment and combine approach for non-parametric time-series classification, In: Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases

    Google Scholar 

  28. Jakuczun W,Wrobel A,Wojcik D, Kublik E (2005) Classifying evoked potentials with local classifiers, Acta Neurobiologiae Experimentalis

    Google Scholar 

  29. Musial P, Kublik E, Wrobel A (1998) Spontaneous variability reveals principal components in cortical evoked potentials, NeuroReport 9:2627–2631

    Article  Google Scholar 

  30. Wypych M, Kublik E, Wojdyllo P, Wrobel A (2003) Sorting functional classes of evoked potentials by wavelets, Neuroinformatic

    Google Scholar 

  31. Wrobel A, Kublik E, Musial P (1998) Gating of the sensory activity within barrel cortex of the awake rat., Experimental Brain Research

    Google Scholar 

  32. Kublik E, Musial P, Wrobel A (2001) Identification of principal components in cortical evoked potentials by brief suface cooling, Clinical Neuropshysiology

    Google Scholar 

  33. Smolinski TG, Boratyn GM, Milanova M, Zurada JM, Wrobel A (2002) Evolutionary Algorithms and Rough Sets-based Hybrid Approach to Classificatory Decomposition of Cortical Evoked Potentials, In: Alpigini JJ, Peters JF, Skowron A, Zhong N (eds.) Rough Sets and Current Trends in Computing, Third International Conference, RSCTC 2002, no. 2475 in Lecture Notes in Artificial Intelligence, 621–628. Springer-Verlag

    Google Scholar 

  34. Kovacevic J, SweldenW(1997). Wavelet families of increasing order in arbitrary dimensions

    Google Scholar 

  35. Valentini G, Dietterich TG (2004) Bias-variance analysis of support vector machines for the development of svm-based ensemble methods., Journal of Machine Learning Research 5:725–775

    MathSciNet  Google Scholar 

  36. Breiman L (2001) Random forests, Machine Learning 45(1):5–32

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Nencki Institute of Experimental Biology, 3 Pasteur Str., 02-093, Warsaw, Poland

    Wit Jakuczun

  2. WLOG Solutions, 1A/25 Harfowa Str., 02-389, Warsaw, Poland

    Wit Jakuczun

Authors
  1. Wit Jakuczun
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, Georgia, 30322, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock, Arkansas, 72204, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, Safat, 13055, Kuwait

    Aboul-Ella Hassanien

  4. Department of Information Technology Faculty of Computers and Information, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul-Ella Hassanien

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Jakuczun, W. (2008). Local Classifiers as a Method of Analysing and Classifying Signals. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Applications of Computational Intelligence in Biology. Studies in Computational Intelligence, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_5

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  • DOI: https://doi.org/10.1007/978-3-540-78534-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78533-0

  • Online ISBN: 978-3-540-78534-7

  • eBook Packages: EngineeringEngineering (R0)

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