Abstract
In this paper, we present a new method of learning a feature selection dictionary for rough classification. In the learning stage, both the n-dimensional learning vectors and the n-dimensional reference vectors are transformed into an m(>n)-dimensional learning vector and the m-dimensional reference vector, respectively, using a current feature selection dictionary. The feature selection dictionary is then successively modified for each learning vector so as to decrease the distance between the learning vector and the m-dimensional reference vector corresponding to the correct category. Furthermore, the feature selection dictionary is modified for each learning vector so as to increase the distance between the learning vector and the m-dimensional reference vector that is the nearest incorrect reference vector of the learning vector. The experimental results showed that our method’s processing time is 9 times faster than that without rough classification, even if the recognition rates are the same.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press (1995)
Wakabayashi, T., Tsuruoka, S., Kimura, F., Miyake, Y.: A Study on Feature Selection for Small Class Classification Problems. Trans. of the IEICE, Vol. J80-D-II, No. 1 (1997) 73–80 (in Japanese)
Kawamura, A., Nitta, T.: Feature-Extraction-Based Character Recognition Using Minimum Classification Error Training. Trans. of the IEICE, Vol. J81-D-II, No. 12 (1998) 2749–2756 (in Japanese)
Sato, A., Yamada, K.: A formulation of learning vector quantization using a new misclassification measure. Proc. of the Fourteenth ICPR (1998) 322–325
Jain, A. K., Duin, R. P. W., Mao, J.: Statistical pattern recognition: A Review Trans. on PAMI, Vol. 22, No. 1 (2000) 4–37
Lerner, B., Guterman, H., Aladjem, M., Dinstein, I.: A comparative study of neural network based feature extration paradigms. Pattern Recognition Letters 20 (1999) 7–14
Jutten, C., Herault, J.: Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24 (1991) 1–10
Bell, A. J., Sejnowski, T. J.: An information-maxmization approach to blind separation and blind deconvolution. Neural Computation, Vol. 7 (1995) 1129–1159
Cardoso, J. F.: Blind signal separation: Statistical principles. Proc. of the IEEE, Vol. 86, No. 10 (1998) 2009–2025 Signal Processing 24 (1991) (1–10)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takahashi, K., Sato, A. (2000). A Learning Method of Feature Selection for Rough Classification. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_12
Download citation
DOI: https://doi.org/10.1007/3-540-45014-9_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67704-8
Online ISBN: 978-3-540-45014-6
eBook Packages: Springer Book Archive