International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Speech Feature Selection of Normal and Autistic children using Filter and Wrapper Approach

  • Received : 2021.05.05
  • Published : 2021.05.30
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Abstract

Two feature selection approaches are analyzed in this study. First Approach used in this paper is Filter Approach which comprises of correlation technique. It provides two reduced feature sets using positive and negative correlation. Secondly Approach used in this paper is the wrapper approach which comprises of Sequential Forward Selection technique. The reduced feature set obtained by positive correlation results comprises of Rate of Acceleration, Intensity and Formant. The reduced feature set obtained by positive correlation results comprises of Rasta PLP, Log energy, Log power and Zero Crossing Rate. Pitch, Rate of Acceleration, Log Power, MFCC, LPCC is the reduced feature set yield as a result of Sequential Forwarding Selection.

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References

  1. Guyon, I., Elisseeff,A. "An Introduction to variable and feature selection", Journal of Machine Learning Research, vol.3, pp 1157-1182, (2003).
  2. Aik ,L.E., Kiang ,L.C., Mohamed ,Z.B., Hong,T.W., "A review on the multivariate statistical methods for dimensional reduction studies", In AIP Conference Proceedings, Perlis, Malaysia, (2009).
  3. Morris,K. , McNicholas,P.D., "Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures". Computational Statistics and Data Analysis, Vol. 97, pp. 133-150, (2016). https://doi.org/10.1016/j.csda.2015.10.008
  4. Lin,Y.W.,Deng,B., Xu ,Q., Yun,Y.H., Liang,Y.Z., "The equivalence of partial least squares and principal component regression in the sufficient dimension reduction framework. Chemometrics and Intelligent Laboratory Systems, Vol. 150, pp. 58-64, (2016). https://doi.org/10.1016/j.chemolab.2015.11.003
  5. Mallick, K., Bhattacharyya, S. " Uncorrelated Local Maximum Margin Criterion: An Efficient Dimensionality reduction Method for Text Classification", Procedia Technology, Vol.4, pp. 370 - 374, (2012). https://doi.org/10.1016/j.protcy.2012.05.057
  6. Jingjie,Y., Wang,X., GU,W. "Speech Emotion Recognition Based on Sparse Representation", Archives of Acoustics, Vol.38, No. 4, pp. 465-470, (2013). https://doi.org/10.2478/aoa-2013-0055
  7. Fletcher, S., Islam, Md..,"Decision Tree Classification with Differential Privacy: A Survey", ACM computing surveys, Vol 52( 4), (2019).
  8. Chandrashekar,G., Sahin,F., "A survey on feature selection Methods", Computer and Electrical Engineering, Elsevier, vol.40, pp 16- 28,( 2014). https://doi.org/10.1016/j.compeleceng.2013.11.024
  9. Hancer, E. "New filter approaches for feature selection using differential evolution and fuzzy rough set theory", Neural Comput & Applic, vol.32, pp.2929-2944 (2020). https://doi.org/10.1007/s00521-020-04744-7
  10. Abramovich,H., "The Vibration Correlation Technique - A reliable nondestructive method to predict buckling loads of thin walled structures", Faculty of Aerospace Engineering, Technion, I.I.T., Vol. 25, (2020).
  11. Reunanen,J., "Overfiltering in Making comparisons between variable selection methods, Journal of Machine Learning Research, vol.3, pp. 1371-1382, 2003.
  12. Fayyad,V., Irani,K.B., "Multi interval discretization of continuous valued attributes for classification learning", In Proc. of 13th International Conference on AI, Morgan Kuffman, Washington, D.C, San Francisco, USA, pp 1022-1027, (1993).
  13. R.avi,K., John,G.H., "Wrappers for feature based selection" Artificial Intelligence, vol.97, pp. 273-324, (1997). https://doi.org/10.1016/S0004-3702(97)00043-X
  14. Tawhid, M.A., Ibrahim, A.M., "Feature selection based on rough set approach, wrapper approach, and binary whale optimization algorithm", International journal of machine learning and cybernetics, Vol. 11(3), (2020).
  15. Pudil, P., Novovicova,J., Kitler,J., "Floating search methods in feature selection", Pattern Recognition Letters, Elsevier, vol.15, pp.1119-1125, (1994). https://doi.org/10.1016/0167-8655(94)90127-9
  16. Blessie,E.C, Keyan,E.K., "Sigmas: A Feature Selection Algorithm using correlation based Methods", Journal of Algorithm and Computational Technology, vol.6, pp 385-394,( 2012). https://doi.org/10.1260/1748-3018.6.3.385