Abstract
In this paper, we exploit a new method of implementing mining classification, i.e., Fisher classification algorithm. In comparison with the decision- tree ID3 algorithm and its improved algorithm that is based on the criterion of choosing the split attributes according to information gain ratios and simple Bayes classification algorithm, we find that Fisher classification algorithm has a higher predictive accuracy and relatively less computation effort. Due to the sensitiveness of these methods mentioned above to noise, we propose a perceptron neural network classification algorithm, which has the stronger noise-rejection ability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Han, J. kamber M.: Data Mining. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Shi, Z.Z.: Knowledge Discovery. University of Tsinghua Press, Beijing (2002)
Liu, X.H.: Optmizational Algorithm of Decision Tree. Software Transaction 9, 797–800 (1998)
Quinlan, R.: Programs for Machine Learning. Morgan Karfmann, San Mateo (1993)
Yang, H., Liu, Q.S., Zhong, B.: Mathematical Statistics. Education Press of China, Beijing (2004)
Wang, X.M.: Applied Multi-analysis. University of Shanghai Finance and Economics Press, Shanghai (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, H., Xu, J. (2005). Classification Algorithms Based on Fisher Discriminant and Perceptron Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_4
Download citation
DOI: https://doi.org/10.1007/11427445_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)