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Combining Feature Subsets in Feature Selection

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Multiple Classifier Systems (MCS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3541))

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Abstract

In feature selection, a part of the features is chosen as a new feature subset, while the rest of the features is ignored. The neglected features still, however, may contain useful information for discriminating the data classes. To make use of this information, the combined classifier approach can be used. In our paper we study the efficiency of combining applied on top of feature selection/extraction. As well, we analyze conditions when combining classifiers on multiple feature subsets is more beneficial than exploiting a single selected feature set.

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References

  1. Jain, A.K., Chandrasekaran, B.: Dimensionality and Sample Size Considerations in Pattern Recognition Practice. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, pp. 835–855. North-Holland, Amsterdam (1987)

    Google Scholar 

  2. Fukunaga, K.: Introduction to Statistical Pattern Recognition, pp. 400–407. Academic Press, London (1990)

    MATH  Google Scholar 

  3. De Veld, D.C.G., Skurichina, M., Witjes, M.J.H., et al.: Autofluorescence and Diffuse Reflectance Spectroscopy for Oral Oncology. Accepted in Lasers in Surgery and Medicine (2005)

    Google Scholar 

  4. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html

  5. Tax, D.M.J., van Breukelen, M., Duin, R.P.W., Kittler, J.: Combining Multiple Classifiers by Averaging or Multiplying? Pattern Recognition 33(9), 1475–1485 (2000)

    Article  Google Scholar 

  6. Ho, T.K.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  7. Freund, Y., Shapire, R.E.: Experiments with a New Boosting Algorithm. In: Proceedings of the 13th International Conference on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  8. Kuncheva, L.I., Bezdek, J.C., Duin, R.P.W.: Decision Templates for Multiple Classifier Fusion: An Experimental Comparison. Pattern Recognition 34(2), 299–314 (2001)

    Article  MATH  Google Scholar 

  9. Kuncheva, L.I.: Combining Pattern Classifiers. In: Methods and Algorithms. Wiley, Chichester (2004)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Skurichina, M., Duin, R.P.W. (2005). Combining Feature Subsets in Feature Selection. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_17

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  • DOI: https://doi.org/10.1007/11494683_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26306-7

  • Online ISBN: 978-3-540-31578-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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