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A DATA SELECTION METHOD FOR PROBABILISTIC NEURAL NETWORKS

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2004

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
In this paper, two performances increasing methods for datasets which have a nonuniform class distribution are presented. The methods are applied to probabilistic neural networks (PNN). Selection of a good training data is the most important issue. Therefore, a new data selection procedure including data exchange and data replication is proposed. After reaching the best accuracy by using the data exchange method, a data replication method is applied to the classes which have relatively less numbers of instances. The methods are applied to the Glass, Escheria Coli (E. coli) and Contact Lenses datasets, which have nonuniform class distributions and better accuracies than the reference works were achieved by PNN using these methods.
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PDF icon arastirmax-data-selection-method-probabilistic-neural-networks.pdf
  • 2
1137-1140
  • English

REFERENCES

References: 

[1] http://www.ics.uci.edu/~mlearn/MLReposito
ry.html , 2003.
[2] G. Agre and I. Koprinska, “Case-based
Refinement of Knowledge Based Neural
Networks”, Proc. of the International
Conference on Intelligent Systems: A
Semiotic Perspective, Gaithersberg, MD,
USA, pp. 221-226, 1996.
[3] D. Ventura and T. R. Martinez, “An
Empirical Comparison of Discretization
Methods”, Proc. Tenth International
Symposium on Computer and Information
Sciences, pp. 443-450, 1995.
[4] A. Holst, The Use of a Bayesian Neural
Network Model for Classification Tasks,
Dissertation Thesis at Stockholm University,
1997.
[5] H. Ruda and M. Snorasson, “Adaptive
Preprocessing for On-line Learning With
Adaptive Resonance Theory (ART)
Networks”, IEEE Workshop on Neural
Networks for Signal Processing, Cambridge,
Massachusetts, USA, 1995.
[6] M. Avci and T. Yildirim, “Classification of
Eschericia Coli Bacteria by Artificial Neural
Networks”, IEEE International Symposium
on Intelligent Systems, Varna, Bulgaria, Vol:
3, pp. 16-20, 2002.
[7] P. Horton and K. Nakai, “A Probabilistic
Classification System for Predicting the
Cellular Localization Sites of Proteins”,
Intelligent System in Molecular Biology,
Vol: 4, pp. 109-115, 1996.
[8] P. Horton and K. Nakai, “Better Prediction
of Protein Cellular Localization Sites With
the k Nearest Neighbors Classifier”,
Intelligent Systems for Molecular Biology,
Vol: 5, pp. 147-152, 1997.

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