Abstract
A new version of Fisher’s discriminant analysis (FDA) is introduced in this paper. Our algorithm searches also for a reduced space in which patterns can be discriminated. However, no intermediate class separability criterion (such as Fisher’s mean distance divided by variance) is used whatsoever. Classification performance is optimized directly. Since no statistical hypothesis are made, the method is of general applicability. Our evolutionary approach for optimization makes the number of projections and classes independent of each other. Even different numbers of projections, not necessarily the means, can be used for each class. As a proof of concept, the UCI thyroid problem (three classes) is solved in one dimension instead of two with state of the art performance and making use of only three of the 21 original features.
This is a preview of subscription content, log in via an institution to check access.
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
Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)
Rao, C.R.: The Utilization of Multiple Measurements in Problems of Biological Classification (with Discussion). Journal of the Royal Statistical Society series B 10, 159–203 (1948)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Beyer, H.G., Schwefel, H.P.: Evolution Strategies. A Comprehensive Introduction Natural Computing 1, 3–52 (2002)
Prechelt, L.: Some Notes on Neural Learning Algorithm Benchmarking. Neurocomputing 9(3), 343–347 (1995)
Sierra, A.: High Order Fisher’s Discriminants. Pattern Recognition 35, 1291–1302 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sierra, A., Echeverría, A. (2003). Skipping Fisher’s Criterion. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_111
Download citation
DOI: https://doi.org/10.1007/978-3-540-44871-6_111
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40217-6
Online ISBN: 978-3-540-44871-6
eBook Packages: Springer Book Archive